i
SUITABILITY MAPPING OF SOLAR POWER
PLANTS USING AN EXPLAINABLE AI-BASED
APPROACH
Mawanane Hewa Rasanka Mangala De Sil a
ii
SUITABILITY MAPPING OF SOLAR POWER PLANTS USING
AN EXPLAINABLE AI-BASED APPROACH
Disse a ion supe ised by:
Leona do Vanneschi, PhD
NOVA In o ma ion Managemen School
Uni e sidade No a de Lisboa
Lisbon, Po ugal
Co-supe ised by:
Ma co Painho, PhD
NOVA In o ma ion Managemen School
Uni e sidade No a de Lisboa
Lisbon, Po ugal
Co-supe ised by:
Michael Gould, PhD
GEOTEC
Uni e si a Jaume I
Cas ellón, Spain
Feb ua y 20, 2024
iii
ACKNOWLEDGMENTS
I ex end my hea el g a i ude o my supe iso , P o . D . Leona do Vanneschi, o
his s ead as suppo , guidance, and in aluable insigh s du ing he s udy. Addi ionally,
I am deeply hank ul o my co-supe iso s, P o . D . Ma co Painho and P o . D .
Michael Gould, o hei aluable guidance, unwa e ing encou agemen , and
consis en suppo h oughou he s udy. Thei expe ise and mo i a ion ha e played a
pi o al ole in shaping he ajec o y o my wo k.
I exp ess my genuine g a i ude o he acul y and s a o Uni e sidade No a de Lisboa,
Uni e si y o Müns e , and Uni e si a Jaume I o hei p o ision o esou ces,
assis ance, and an en i onmen conduci e o academic explo a ion. Addi ionally, I
would like o hank he E asmus Mundus p og am o i s unding suppo owa ds my
Mas e o Science in Geospa ial Technologies.
I am hank ul o my amily in S i Lanka o hei unwa e ing suppo in e e y aspec ,
p opelling me o wa d. Las , I hank my wi e, P asadi, o he cons an encou agemen
and s ead as suppo h oughou he s udy. You mo i a ion has been indispensable in
enabling me o comple e his s udy success ully.
This hesis ep esen s he culmina ion o he combined e o s, encou agemen , and
suppo om nume ous indi iduals, and o his, I am since ely g a e ul.
i
SUITABILITY MAPPING OF SOLAR POWER PLANTS USING
AN EXPLAINABLE AI-BASED APPROACH
ABSTRACT
The con en ional app oaches o iden i ying op imal loca ions o sola powe plan s
a e adi ionally handled h ough Mul i-C i e ia Decision-Making (MCDM)
echniques, which o en su e om subjec i i y and lack o anspa ency. Al hough
ecen ad ancemen s in Machine Lea ning (ML) ha e o e ed po en ial solu ions o
MCDM-based me hods, adi ional ML models s uggle o explain hei p edic ions
and ope a e wi hou assuming ha cu en sola powe plan s a e in op imal loca ions.
Thus, his esea ch ep esen s an in eg a ed explainable AI app oach wi h ML
me hods, aiming o enhance he anspa ency and comp ehensibili y o esul s and
in oduce a no el pa adigm by employing he classi ied e iciency o exis ing plan s,
ca ego ized in o i e classes, as he dependen a iable o ML models, he eby
challenging he p e alen assump ion inhe en in con en ional ML models. Twel e
independen a iables selec ed h ough a li e a u e e iew we e used, along wi h he
classi ied e iciency alues, o ain i e ML models, namely, Random Fo es (RF),
Suppo Vec o Machines (SVM), Mul i-laye Pe cep on (MLP), Decision ee (DT),
and K-nea es neighbo s (k-NN).
Following he assessmen o he models' accu acies, he RF model, which achie ed
88% o e all accu acy, was subsequen ly explained using SHapley Addi i e
exPlana ions (SHAP), e ealing Sola Radia ion (13%) and Cloud Index (12%) as he
mos in luen ial a iables o he esul ing p edic ions. In compa ison, Aspec (5%)
was iden i ied as he leas signi ican pa ame e o he model p edic ions.
The inal sola sui abili y map p oduced wi h he supe io RF model iden i ied
app oxima ely 5% o he o al land a ea in he USA as highly sui able o cons uc ing
sola powe plan s, ensu ing op imal ope a ional e iciency. Addi ionally, 55% o he
land is mode a ely sui able o such es ablishmen s. Con e sely, app oxima ely 9.5%
o he o al land a ea, equi alen o 766,654 km2, is deemed pe manen ly unsui able
o sola powe plan cons uc ion.
Sus ainable De elopmen Goals (SGD):

i
KEYWORDS
Renewable Ene gy
Geog aphical In o ma ion Sys ems
Machine Lea ning
Explainable A i icial In elligence
Sui abili y Mapping
Uni ed S a es o Ame ica
ii
ACRONYMS
A – Aspec
AHP – Analy ic Hie a chy P ocess
AI – A i icial In elligence
ANN – A i icial Neu al Ne wo k
AT – Ai empe a u e
AUC – A ea unde he Cu e
AUROC – A ea Unde he ROC cu e
CI – Cloud index
CSV – Comma-sepa a ed alues
DT – Decision T ee
E – Ele a ion
EIA – Ene gy In o ma ion Agency
GIS – Geog aphic In o ma ion Sys ems
GW – Gigawa
k-NN – k-Nea es Neighbo
LULC – Land use/ land co e
MCDM – Mul i-C i e ia Decision Making
MDI – Mean Dec ease in Impu i y
ML – Machine lea ning
MLP – Mul i-laye Pe cep on
MW – Megawa
PC – P oximi y o he ci y cen e
PD – Popula ion densi y
iii
PG – P oximi y o g id
PR – P oximi y o oad ne wo k
RF – Random Fo es
ROC – Recei e Ope a ing Cha ac e is ic
S – Slope
SHAP – Shapley addi i e explana ions
SMOTE – Syn he ic Mino i y O e sampling Technique
SMV – Suppo Vec o Machines
SR – Sola adia ion
Ti – Tole ance
TOPSIS – Technique o O de Pe o mance by Simila i y o Ideal Solu ion
USA – Uni ed S a es o Ame ica
VIF – Va iance In la ion Fac o
WS – Wind speed
XAI – Explainable A i icial In elligence
ix
INDEX OF THE TEXT
Page
ACKNOWLEDGMENTS ................................................................................................... iii
ABSTRACT ........................................................................................................................ i
KEYWORDS ....................................................................................................................... i
ACRONYMS ...................................................................................................................... ii
INDEX OF THE TEXT ....................................................................................................... ix
INDEX OF TABLES ........................................................................................................... xi
INDEX OF FIGURES ........................................................................................................ xii
1. INTRODUCTION ............................................................................................. 13
1.1 O e iew o he wo k ................................................................................................... 13
1.2 Resea ch gap ................................................................................................................ 15
1.3 Objec i es ..................................................................................................................... 15
1.4 Thesis o ganiza ion ...................................................................................................... 16
2. LITERATURE REVIEW .................................................................................. 17
2.1 Sola sui abili y mapping ............................................................................................. 17
2.2 A i icial neu al ne wo ks-based app oaches o sola sui abili y mapping ................. 20
2.3 Machine lea ning o e MCDM-based me hods ........................................................... 21
2.4 Explainable AI in sola sui abili y mapping ................................................................. 22
2.5 C i e ia and es ic ion ac o s ...................................................................................... 24
2.6 Renewable ene gy in he Uni ed S a es o Ame ica ..................................................... 27
3. DATA AND METHODOLOGY....................................................................... 28
3.1 S udy a ea and Da a...................................................................................................... 28
3.2 Me hodology ................................................................................................................ 29
3.2.1 Da a p e-p ocessing .................................................................................................. 31
3.2.2 Machine lea ning models .......................................................................................... 33
3.2.2.1 Random Fo es classi ie (RF) ............................................................................ 34
3.2.2.2 Suppo Vec o Machines (SVM) ....................................................................... 34
3.2.2.3 Decision T ee (DT)............................................................................................. 34
3.2.2.4 k-Nea es Neighbo s (k-NN) .............................................................................. 34
3.2.2.5 Mul i-Laye Pe cep on (MLP) .......................................................................... 35
3.2.3 Model e alua ion, alida ion, and selec ion .............................................................. 35
3.2.3.1 Recei e Ope a ing Cha ac e is ic (ROC) cu es .............................................. 36
3.2.3.2 Accu acy ............................................................................................................. 36
3.2.3.3 P ecision ............................................................................................................. 36
3.2.3.4 F1 sco e .............................................................................................................. 36
3.2.4 Explainable AI .......................................................................................................... 37
3.2.5 Sola powe plan sui abili y map ............................................................................. 38
4. RESULTS AND DISCUSSION .................................................................................. 39
4.1 Resul s .......................................................................................................................... 39
Chap e 1. In oduc ion
16
1. Wha a e he key ac o s ha signi ican ly in luence he sui abili y o sola
powe plan si e selec ion in di e en egions in he USA?
2. Which ML-based classi ica ion me hod is he mos accu a e o c ea ing a
sui abili y map o iden i y po en ial sola powe si es using exis ing sola
p oduc i i y da a?
3. Wha is he in luence o each ac o on he inal sui abili y map?
The esul s o his esea ch ha e he po en ial o p o ide aluable insigh s o decision-
make s in ol ed in u ban planning, ene gy policy de elopmen , and en i onmen al
conse a ion ini ia i es. Doing so can p omo e a cleane , mo e sus ainable
en i onmen and a g eene u u e o u u e gene a ions.
1.4 Thesis o ganiza ion
This hesis comp ises a o al o six subsequen chap e s. Chap e 1 is an in oduc o y
chap e , p o iding an o e iew o he esea ch, emphasizing he esea ch gap, and
ou lining he p ojec 's objec i es. Mo ing on o Chap e 2, a comp ehensi e e iew o
he exis ing li e a u e is conduc ed, ocusing on echniques ela ed o sui abili y
mapping o sola powe plan s and ML and explainable AI-based me hods. Chap e 3
del es in o he s udy a ea, explaining he da a se s employed and de ailing he
me hodology and ools u ilized in he esea ch. Chap e 4 is dedica ed o p esen ing
he esea ch indings, o ganized wi h a comp ehensi e analysis and discussion o he
ob ained esul s. Finally, Chap e 5 se es as a concluding chap e , consolida ing he
esea ch by summa izing he key indings, add essing esea ch ques ions, and o e ing
ecommenda ions o u u e endea o s.

17
2. LITERATURE REVIEW
In he mission o educing ca bon emissions and conse ing ini e ene gy esou ces,
such as ossil uels, which a e consumed a a a e highe han hei na u al
eplenishmen , nume ous coun ies a ound he wo ld ha e s a egized he adop ion o
al e na i e enewable ene gy sou ces as a means o mee ing he ene gy demands o he
21s cen u y (He nandez, 2018). Among hese na ions, hose loca ed in a id and semi-
a id clima ic egions ha e exhibi ed a pa icula in e es in consuming sunligh as a
esou ce due o i s abundan and eely a ailable na u e (Halde e al., 2022). Gi en
he complica ed ela ionship be ween he loca ion o sola powe plan s, hei
e iciency, and he associa ed cons uc ion cos s, he p ac ice o sui abili y mapping
o such ins alla ions has ga he ed subs an ial a en ion (Spy idonidou & Vagiona,
2023). This li e a u e e iew syn hesizes exis ing esea ch on he subjec , speci ically
ocusing on using explainable AI echniques o de e mine he mos sui able loca ions
o sola powe plan s.
2.1 Sola sui abili y mapping
Sola sui abili y mapping in ol es he iden i ica ion o geog aphically op imal egions
o es ablishing sola ene gy gene a ion in as uc u e (Sup o a e al., 2020). I
comp ehensi ely analyzes a ious in luen ial ac o s, such as sola i adiance le els,
land a ailabili y, clima ic condi ions, and economic iabili y (Mahe e al., 2015a;
Rida e al., 2017; Te uel-Solano e al., 2013). Changes in hese ac o s can esul in
some egions on Ea h's su ace being mo e o less sui able o es ablishing sola powe
plan s (Alami Me ouni e al., 2018). Typically, he exis ing li e a u e e eals ha a eas
wi h high sola i adiance le els, minimal en i onmen al limi a ions, and p oximi y o
es ablished powe in as uc u e a e a o ed o sola powe plan ins alla ions (Asadi
e al., 2023; Asadi & Pou hossein, 2021; Bayounis & Eldama y, 2022; Halde e al.,
2022). None heless, he p ecise c i e ia o sui abili y can di e ac oss s udies,
Chap e 1. In oduc ion
18
mi o ing he a ia ions in local condi ions and p io i ies (Sup o a e al., 2020).
Iden i ying hese sui able egions is a complex unde aking ha equi es he
simul aneous e alua ion and compa ison o mul iple in luencing ac o s h ough a
sys ema ic p ocedu e (Halde e al., 2022; He nandez, 2018; Husein e al., 2023).
Following he in oduc ion o Geog aphical In o ma ion Sys ems (GIS), adi ional
esea ch me hodologies ha e u ilized GIS-based Mul i C i e ia Decision Making
(MCDM) echnologies o add ess hese inqui ies (Sup o a e al., 2020). This p ocess
en ails he c ea ion o dis inc spa ial laye s ha ep esen a ious in luencing ac o s,
subsequen ly con e ed in o spa ial decisions. A weigh ed o e lay is hen applied o
hese laye s by assigning speci ic weigh s o each inpu . The de e mina ion o hese
weigh s in ol es e alua ing he signi icance o he ac o s using a a ie y o
echniques, such as Analy ical Hie a chical P ocess (AHP) (Halde e al., 2021),
(Alami Me ouni e al., 2018), (Mahe e al., 2015b), Technique o O de o
P e e ence by Simila i y o ideal Solu ion (TOPSIS) (Sa kodie e al., 2022), and
Boolean uzzy logic models (Youse i e al., 2018), as well as he expe opinions om
he ield. Table 2.1 summa izes he me hodologies used in esea ch ha apply MCDM
echnologies o map he sui abili y o sola powe plan s.
Me hods applied
Re e ences
1
Da a En elopmen Analysis (DEA) and G ey
Based Mul iple C i e ia Decision Making
(G-MCDM)
(Wang e al., 2022)
2
Linea Reg ession Modelling (LRM) and
GIS + AHP
(Asadi e al., 2023)
3
AHP + Sensi i i y analysis (SA)
(Rida Azmi, Hicham Ama , 2017)
4
GIS + MCDM
(Islam e al., 2022), (Ghe boudj & Ghedi a,
2016), (Villac eses e al., 2022)
5
Technique o o de o p e e ence by simila i y
o ideal solu ion (TOPSIS), Complex
p opo ional assessmen (COPRAS), Mul i-
objec i e op imiza ion on a io analysis
(MOORA) + Spea man co ela ion coe icien
(SCC)
(Sa kodie e al., 2022)
6
MCDM + TOPSIS
(Sánchez-Lozano e al., 2015), (Te uel-
Solano e al., 2013)
7
MCDM
(Nzelibe e al., 2022), (Akkas e al., 2017)
8
MCDM +AHP
(Munkhba & Choi, 2021), (Tisza, 2014),
(Wa son & Hudson, 2015), (Bayounis &
Chap e 1. In oduc ion
19
Eldama y, 2022), (Wa son & Hudson,
2015)
9
GIS + AHP
(Halde e al., 2021), (Alami Me ouni e
al., 2018), (Mahe e al., 2015b)
10
GIS + MCDM +AHP
(Halde e al., 2022)
11
GIS + Boolean and uzzy model
(Youse i e al., 2018)
Table 2.1: MCDM based Sola powe plan sui abili y me hods used in p e ious s udies
None heless, he esul s o hese esea ches depend upon a ious ac o s, such as he
selec ion o c i e ia, he assignmen o ela i e weigh s o he selec ed c i e ia, and he
pa icula pai s o c i e ia used o assessing hei ela i e impo ance (Asadi &
Pou hossein, 2021; Husein e al., 2023; Sánchez-Lozano e al., 2015). The e o e, he
conclusions d awn om hese weigh -based echniques a e subjec i e and suscep ible
o he esea che 's pe sonal bias, expe ise, and p ac ical expe ience in he ield (Sun
e al., 2023a).
To illus a e his a iabili y, conside wo s udies conduc ed o assess he sui abili y o
sola powe plan loca ions in Mo occo, namely, (Taou ik e al., 2021) and (Alami
Me ouni e al., 2018). These s udies iden i ied 11 and 8 in luencing ac o s,
espec i ely. Due o di e ing judgmen s ega ding he selec ion o in luencing ac o s,
he wo s udies assigned a ying deg ees o in luence o hei p ima y ac o , global
ho izon al sola adia ion. In Taou ik's me hodology, his ac o held a 26% in luence
on sola si e selec ion when applying he AHP me hod. In con as , Alami concluded
ha di ec no mal i adia ion was mo e han 50% signi ican in he AHP model. In
addi ion, hese wo s udies used di e en le els o impo ance o he in luencing
ac o s. Fo example, Taou ik conside ed sola adia ion abo e 5 kW/m² highly
sui able, while Alami conside ed 2.1 kW/m² he highes sui able ange. The e o e, he
indings o hese wo s udies de ia e signi ican ly and canno be eadily compa ed o
de e mine he mos accu a e esul s o decision-making p ocesses. As such, he choice
o pa ame e s and sco ing me hods g ea ly in luences p ojec ou comes and is subjec
o he esea che 's biases, le el o knowledge, and eal-wo ld expe ience in he ield.
Due o hese limi a ions and ad ancemen s in A i icial Neu al Ne wo ks (ANN),
esea che s ha e adop ed ML models o ackle loca ion-based challenges (Ghimi e e
al., 2022; Rangel-Ma inez e al., 2021).
Chap e 1. In oduc ion
20
2.2 A i icial neu al ne wo ks-based app oaches o sola
sui abili y mapping
The popula i y o ANN, inspi ed by human biological neu ons, has inc eased as a ool
o da a analysis in a ious applica ions (Singh, 2019), including sui abili y mapping
in GIS. Machine lea ning is a sub ield o a i icial in elligence ha in ol es he
de elopmen o ma hema ical o s a is ical algo i hms ha can lea n om and make
p edic ions o decisions based on exis ing da a abou a speci ic eal-wo ld inqui y
(Sa anya & Subhashini, 2023). The u iliza ion o machine lea ning models o assess
loca ional sui abili y has demons a ed a success ul his o y o me hodologies ac oss
a ious ields in ecen yea s, including p edic ing sui able si es o dams, hospi als,
solid was e land ills, and enewable ene gy si es. These s udies ha e used se e al
machine lea ning echniques, summa ized in Table 2.2, including decision ees,
Random Fo es s, suppo ec o machines, and neu al ne wo ks.
Me hods applied
Re e ences
1
GIS + Machine lea ning (Fuzzy membe ship + Fuzzy
logic model)
(Hai aa Nasse Hussein, Noo
Hashim Hamed, 2023)
2
ML (Hyb id deep CNN-SVR algo i hm)
(Ghimi e e al., 2022)
3
GIS + ML(ANN)
(Oyewola e al., 2022)
4
MCDM + ANN
(Asadi & Pou hossein, 2021)
5
ML (ANN, Suppo ec o eg ession (SVR), and
Gaussian P ocess Reg ession (GPR))
(Sha i zadeh e al., 2019)
6
ML (G adien Boos Decision T ee (GBDT) and eX eme
G adien Boos ing (XGBoos )
(Hou e al., 2023)
7
ML (RF, DT, SVM, k-NN, ANN)
(Shahab & Singh, 2019)
8
GIS + ML + XAI
(Sachi e al., 2022a)
9
ML (RF, MLP, X eme G adien Boos ing (XGBoos ) +
XAI
(Sun e al., 2023b)
Table 2.2: ML based sola powe plan sui abili y me hods used in p e ious s udies
Fo ins ance, p io esea ch conduc ed by Shahab examines he e ec i eness o i e
machine lea ning algo i hms (Random Fo es (RF), Decision T ee (DT), Suppo
Vec o Machines (SVM), K-Nea es neighbo s (k-NN), and A i icial Neu al Ne wo k
(ANN)) o classi ying he sui abili y o enewable ene gy si es based on geophysical
da a se s (Shahab & Singh, 2019). The s udy's indings e ealed ha he RF (F1 sco e
o 92%) algo i hm demons a ed he mos ema kable pe o mance, whe eas he SVM
(F1 sco e o 85%) showed he leas accu a e esul s. Ano he s udy conduc ed by Asadi
Chap e 1. In oduc ion
21
employed a combina ion o MCDM and Mul i-Laye Pe cep on (MLP) o de e mine
he sui abili y o sola powe plan loca ions in I aq (Asadi & Pou hossein, 2021). The
s udy's indings u he alida e he sui abili y o machine lea ning-based me hods,
o e ing accu a e and s able esul s o implemen ing global sco ing capabili ies in
sui abili y analysis asks. Howe e , he assessmen o he supe io i y o machine
lea ning-based me hods o e adi ional MCDM-based app oaches needs o be
add essed.
2.3 Machine lea ning o e MCDM-based me hods
A s udy conduc ed by Saha examined he e ec i eness o MCDM-based AHP and
Fuzzy Complex P opo ional Assessmen (FCOP-RAS) me hods o e wo machine
lea ning app oaches: The Random Fo es (RF) algo i hm and he Mul ilaye
Pe cep on (MLP) o he sui abili y analysis ask using i een in luencing c i e ia
collec ed h ough g ound su eys. The esul s o he s udy showed ha he accu acy
o he models de eloped using machine lea ning based model achie ed highe AUC
sco es compa ed o he o he me hods, wi h AUC sco es o 0.947 o RF, 0.923 o
AHP, 0.928 o FCOP-RAS, and 0.932 o MLP. Addi ionally, he s udy highligh ed
he ime-sa ing a ibu e o he machine lea ning-based me hod o e MCDM-based
app oaches due o he lowe human in e ac ion o ask execu ion (Saha & Mondal,
2022).
Addi ionally, a ecen s udy conduc ed a s a e-o - he-a analysis o ML-based and
MCDM-based me hods o decision suppo sys ems e ealed ha he ML-based
decision-making sys ems p o ide obus and e icien solu ions o complex p oblems
by e ec i ely handling la ge and complex da ase s compa ed o he adi ional
MCDM-based me hods. Mo eo e , he s udy also highligh ed he abili y o ML
algo i hms o lea n om da a pa e ns and make p edic ions wi hou explici
p og amming, hus educing he subjec i e judgmen s on hose decision-making
s udies (Ali e al., 2023).
Fu he mo e, a s udy conduc ed by Asadi has highligh ed one o he signi ican
disad an ages o MCDM-based app oaches known as local sco ing p ope y, which
e e s o he limi a ion, whe ein sco es in MCDM me hods a e de i ed om a es ic ed

Chap e 1. In oduc ion
22
se o local obse a ions, esul ing in a lack o obus ness and suscep ibili y o changes
in sco es when new candida es a e in oduced. As a solu ion o his issue, Asadi and
colleagues in oduced a no el ML-based app oach using Mul ilaye Pe cep on
(MLP), which o e s obus and global sco ing solu ions o modeling wind/sola a m
si ing in Eas Aze baijan. This s udy u he con i ms he ad an ages o ML-based
me hods o e he adi ional MCDM-based me hods in he con ex o sui abili y
analysis p ojec s. Howe e , ML-based me hods a e p o en o be mo e accu a e and
e icien , and he a ailabili y o accu a ely labeled g ound u h samples is an essen ial
ac o o he success ul aining o machine lea ning models (Ghimi e e al., 2022),
(Singh, 2019), (Sachi e al., 2022a).
Addi ionally, he s udies ha used he ML-based me hod o loca ional sui abili y
mapping ha e achie ed accu a e esul s and a e capable o sol ing he issues inhe i ed
wi h he adi ional MCDM-based me hods; hey o en ail o explain he eason behind
he ou pu esul s o loca ion selec ions (Sachi e al., 2022a). These s udies usually
ely on accu acy measu es such as a ea unde he cu e (AUC) o accu acy unde he
ecei e ope a ing cha ac e is ics (AUROC) calcula ed o he alida ion da a as he
p ima y basis o he selec ions, wi hou p o iding insigh s, such as whe he he chosen
egions ha e highe insola ion o lowe slopes, o ice e sa (P ana R, Shashank T
K, n.d.; Sa anya & Subhashini, 2023). As a po en ial solu ion, Explainable AI-based
app oaches ha e been inco po a ed in o loca ion sui abili y s udies o p o ide a
anspa en unde s anding o how each inpu ac o in luences and con ibu es o he
AI model's ou pu s.
2.4 Explainable AI in sola sui abili y mapping
Explainable A i icial In elligence (XAI) is a concep pionee ed by he De ense
Ad anced Resea ch P ojec s Agency (DARPA) o explain he in e nal p ocess o an
AI model (Sa anya & Subhashini, 2023). I in ol es p o iding use -unde s andable
explana ions o he me hod, he p ocess, and he ou pu o an ML model. I simply
con e s he Blackbox na u e o an ML model o a Whi ebox (Gohel e al., 2021). The
explana ions p o ided by XAI a e ca ego ized in o wo componen s: Knowledge-
d i en XAI and Da a-d i en XAI. Knowledge-d i en XAI is employed o acqui e
Chap e 1. In oduc ion
23
insigh s in o he me hodology and echniques used. A he same ime, da a-d i en XAI
helps unde s and he impac and con ibu ion o each inpu ea u e on he AI models'
ou comes. A s udy conduc ed by Sa anya has iden i ied a wide ange o applica ion
domains whe e XAI has been employed in pas esea ch, including heal hca e,
ag icul u al planning, inance, o ecas ing, social media, and mo e (Sa anya &
Subhashini, 2023). Sachi acknowledged ha hei s udy ma ked he ini ial a emp o
employ an Explainable AI-based app oach in mapping spa ial sui abili y o global
wind and sola sys ems o e alua e he speci ic con ibu ion o each inpu c i e ion
wi hin he ML model (Sachi e al., 2022a). The s udy highligh s compelling scien i ic
e idence suppo ing machine lea ning models' obus ness, accu acy, and applicabili y
in sui abili y analysis p ojec s. The pe o mance me ics o he chosen models,
including Random Fo es (RF), Mul i-Laye Pe cep on (MLP), and Suppo Vec o
Machine (SVM), demons a ed an accu acy exceeding 70% ac oss all models.
No ably, RF eme ged as he mos sensi i e and speci ic algo i hm, achie ing a
sensi i i y o 0.88 and a speci ici y o 0.91. The algo i hm's o e all accu acy and kappa
coe icien we e 0.90 and 0.78, espec i ely, while he A ea Unde he Cu e eached
0.96. These esul s unde sco e he e ec i eness o ML models, mainly RF, in
enhancing he p ecision and eliabili y o sui abili y analyses. Fu he , he s udy also
u ilized he global and local explana ions o he SHapley Addi i e exPlana ions
(SHAP) me hod o explain he ML model. The s udy's indings e ealed ha ac o s
such as dis ance om ci y cen e s and empe a u e signi ican ly in luenced sui abili y
decisions wi hin he ML model.
A sepa a e s udy conduc ed by Sun modeled he loca ion choice o la ge-scale sola
pho o ol aic powe plan s in China using in e p e able machine-lea ning echniques.
This esea ch e ealed ha he wo mos c i ical p edic o s o sui abili y o sola
pho o ol aic ins alla ion loca ions we e consis en ly he ege a ion index and he
dis ance o he powe g id among he selec ed 21 geospa ial condi ioning ac o s (Sun
e al., 2023a). Ne e heless, i is essen ial o no e ha bo h o hese s udies ope a ed
assuming ha he cu en sola powe plan s a e si ua ed in he mos sui able a eas
wi hin hei espec i e egions (Sachi e al., 2022a; Sun e al., 2023a). Howe e , his
assump ion may no hold in many ins ances. The li e a u e men ioned abo e has also
shown ha using XAI echniques in sola powe plan selec ion p ojec s is s ill
Chap e 1. In oduc ion
24
ela i ely unde - esea ched. Only a ew s udies ha e been ca ied ou in he pas ha
in eg a ed he spa ial loca ions o he exis ing plan s o he ML model (Sun e al.,
2023a). To he bes o ou knowledge, no s udies conside ed he e iciency o p e ious
acili ies o he ML model.
2.5 C i e ia and es ic ion ac o s
The ca e ul selec ion o c i e ia and es ic ion ac o s is a c ucial aspec o a sola
powe plan sui abili y mapping p ojec , as i di ec ly in luences he accu acy and
ele ance o he esul s (Sup o a e al., 2020). The e iciency o sola powe plan s is
in luenced by he en i onmen al and module ea u es o he loca ion and he equipmen
deployed in he sola powe plan s (Sahin e al., 2023). Table 2.3 and Table 2.4
summa izes he c i e ia and es ic ion ac o s in he p e ious s udies examined in his
esea ch.
C i e ia/
In luen ial ac o
Re e ences
1
Sola adia ion
(Wang e al., 2022), (Asadi e al., 2023), (Bayounis & Eldama y, 2022),
(Ghe boudj & Ghedi a, 2016), (Shahab & Singh, 2019), (Sánchez-
Lozano e al., 2015), (Te uel-Solano e al., 2013), (Villac eses e al.,
2022), (Nzelibe e al., 2022), (Munkhba & Choi, 2021), (Tisza, 2014),
(Halde e al., 2022), (Alami Me ouni e al., 2018), (Khandaka e al.,
2019), (Akkas e al., 2017), (Sachi e al., 2022a), (Rida Azmi, Hicham
Ama , 2017), (Sahin e al., 2023), (Wa son & Hudson, 2015), (Sun e al.,
2023b), (Youse i e al., 2018), (He nandez, 2018)
2
Ai empe a u e
(Ghe boudj & Ghedi a, 2016), (Shahab & Singh, 2019), (Sánchez-
Lozano e al., 2015), (Te uel-Solano e al., 2013), (Villac eses e al.,
2022), (Munkhba & Choi, 2021), (Halde e al., 2021), (Khandaka e al.,
2019), (Oyewola e al., 2022), (Sachi e al., 2022a), (Rida Azmi, Hicham
Ama , 2017), (Sahin e al., 2023), (He nandez, 2018)
3
Wind speed
(Wang e al., 2022), (Asadi e al., 2023), (Ghe boudj & Ghedi a, 2016),
(Villac eses e al., 2022), (Khandaka e al., 2019), (Asadi & Pou hossein,
2021), (Sachi e al., 2022a), (Sahin e al., 2023), (Wa son & Hudson,
2015)
4
P ecipi a ion
(Wang e al., 2022), (Oyewola e al., 2022)
5
Rela i e humidi y
(Wang e al., 2022), (Ghe boudj & Ghedi a, 2016), (Khandaka e al.,
2019), (Oyewola e al., 2022), (Sahin e al., 2023), (Youse i e al., 2018)
6
Sunshine hou
(Wang e al., 2022), (Akkas e al., 2017), (Sun e al., 2023b)
7
Slope
(Asadi e al., 2023), (Bayounis & Eldama y, 2022), (Islam e al., 2022),
(Shahab & Singh, 2019), (Sánchez-Lozano e al., 2015), (Te uel-Solano
e al., 2013), (Nzelibe e al., 2022), (Munkhba & Choi, 2021), (Halde e
al., 2021), (Alami Me ouni e al., 2018), (Asadi & Pou hossein, 2021),
Chap e 1. In oduc ion
25
(Sachi e al., 2022a), (Rida e al., 2017), (Sun e al., 2023b), (Youse i e
al., 2018), (He nandez, 2018)
8
Cloud Index
(Wang e al., 2022), (Sachi e al., 2022a)
9
Technical
in o ma ion and
assis ance
(Wang e al., 2022)
10
Geology
(Wang e al., 2022)
11
Skilled manpowe
a ailabili y
(Wang e al., 2022)
12
Elec ici y demand
(Wang e al., 2022), (Sun e al., 2023b)
13
Land Cos s
(Wang e al., 2022), (Sun e al., 2023b), (Sun e al., 2023b)
14
Local esiden
a i ude
(Wang e al., 2022)
15
Go e nmen
policies and laws
(Wang e al., 2022), (Sun e al., 2023b)
16
Land use/ land
co e
(Wang e al., 2022), (Islam e al., 2022), (Sánchez-Lozano e al., 2015),
(Te uel-Solano e al., 2013), (Villac eses e al., 2022), (Nzelibe e al.,
2022), (Tisza, 2014), (Halde e al., 2021), (Oyewola e al., 2022), (Asadi
& Pou hossein, 2021), (Sachi e al., 2022a), (Rida Azmi, Hicham Ama ,
2017), (Sun e al., 2023b), (Youse i e al., 2018)
17
Suppo
mechanisms
(Wang e al., 2022)
18
Wildli e and
endange ed species
impac
(Wang e al., 2022)
19
Ha m ul oxin
emission
(Wang e al., 2022)
20
Ene gy sa ing
bene i s
(Wang e al., 2022)
21
T ansmission g id
accessibili y
(Wang e al., 2022), (Asadi e al., 2023), (Bayounis & Eldama y, 2022),
(Islam e al., 2022), (Sánchez-Lozano e al., 2015), (Te uel-Solano e al.,
2013), (Villac eses e al., 2022), (Nzelibe e al., 2022), (Munkhba &
Choi, 2021), (Tisza, 2014), (Alami Me ouni e al., 2018), (Sachi e al.,
2022a), (Rida Azmi, Hicham Ama , 2017), (Wa son & Hudson, 2015),
(Sun e al., 2023b), (He nandez, 2018)
22
P oximi y o oad
ne wo k
(Wang e al., 2022), (Asadi e al., 2023), (Bayounis & Eldama y, 2022),
(Islam e al., 2022), (Sánchez-Lozano e al., 2015), (Te uel-Solano e al.,
2013), (Villac eses e al., 2022), (Munkhba & Choi, 2021), (Tisza, 2014),
(Halde e al., 2021), (Alami Me ouni e al., 2018), (Asadi &
Pou hossein, 2021), (Sachi e al., 2022a), (Rida Azmi, Hicham Ama ,
2017), (Wa son & Hudson, 2015), (Sun e al., 2023b), (Youse i e al.,
2018), (He nandez, 2018)
23
Residen ial a eas/
Popula ion densi y
(Wang e al., 2022), (Nzelibe e al., 2022), (Alami Me ouni e al., 2018),
(Sachi e al., 2022a), (Wa son & Hudson, 2015), (Sun e al., 2023b)
24
P oximi y o ci y/
u ban
(Asadi e al., 2023), (Bayounis & Eldama y, 2022), (Islam e al., 2022),
(Sánchez-Lozano e al., 2015), (Villac eses e al., 2022), (Asadi &
Chap e 3. Da a and Me hodology
32
𝐸𝑓𝑓𝑖𝑒𝑐𝑖𝑒𝑛𝑐𝑦 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑛𝑒𝑡 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛
365 𝑑𝑎𝑦𝑠∗24 ℎ𝑜𝑢𝑟𝑠∗𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ∗ 100% ......................... (3.1)
Due o he lack o exis ing li e a u e p o iding cu -o alues o e iciency ca ego ies
in sola powe plan s, hypo he ical assump ions we e made o classi ica ion as
ou lined below.
I he e iciency exceeded 50%, i indica ed ha he powe plan ope a ed op imally
o a leas 12 hou s a day. Based on his, powe plan s wi h an e iciency alue o
mo e han 40% we e ca ego ized as hose loca ed in he mos sui able geog aphical
a eas, and he powe plan s wi h e iciencies below 1% we e conside ed indica i es o
he pe manen ly unsui able egions. Subsequen ly, he emaining sola powe plan s
we e ca ego ized in o h ee classes based on hei e iciency le els. Powe plan s wi h
e iciencies anging om 1 o 14 we e classi ied as ma ginally no sui able, hose wi h
e iciencies om 14 o 27 we e classi ied as ma ginally sui able, and hose wi h
e iciencies om 27 o 40 we e classi ied as mode a ely sui able. The uppe alue o
each class was no included in he ange. Once he geoda abase was es ablished, he
subsequen p ocessing s eps we e implemen ed o ende i sui able o he ML model.
• Handling missing alues:
Missing alues in he geoda abase we e add essed by eplacing hem wi h he mean
and modes o he a ailable da a, u ilizing he mode o land use, land co e , aspec ,
popula ion densi y, and he mean o he emaining a iables.
• Collinea i y and Mul icollinea i y assessmen :
In o de o mi iga e he in luence o edundan a iables on he ML model, an
e alua ion o collinea i y and mul icollinea i y was unde aken. This in ol ed
gene a ing a co ela ion ma ix and compu ing he a iables' Tole ance (Ti) and
Va iance In la ion Fac o (VIF). The analysis signals mul icollinea i y conce ns when
T is below 0.1, and VIF exceeds 10. Addi ionally, collinea i y issues a e iden i ied
when he co ela ion app oaches ±1.
• Da a spli ing:
The da ase was hen spli in o wo classes o he aining and es ing da ase s,
ollowing he a io o 8:2, consis en wi h o he s udies (Sachi e al., 2022a), (Sun e

Chap e 3. Da a and Me hodology
33
al., 2023a). The ML models we e ained using he aining da ase , and he accu acy
o he models was assessed using he es ing da ase .
• Da a balancing:
Due o he unequal numbe o obse a ions ac oss e iciency classes, he Syn he ic
Mino i y O e sampling Technique (SMOTE) was employed o duplica e obse a ions
in he mino i y class, ensu ing he impa iali y o he ML models.
• Da a no maliza ion (T aining da a se ) and Rescaling (Tes ing da a se ):
Gi en he wide a ia ion in uni s among he independen a iables, a minimum-
maximum no maliza ion me hod was u ilized o s anda dize he aining da a a ia ion
om 0 o 1 o each a iable using he calcula ed minimum and maximum alues
ob ained exclusi ely o he aining da a se . Then, he calcula ed minimum and
maximum alues we e used o escale he es ing da a se o educe he a o emen ioned
wide a ia ions o he es ing da a se .
3.2.2 Machine lea ning models
ML models a e designed o lea n pa e ns and ela ionships om labeled da a, enabling
hem o make p edic ions on new, unseen da a. The wo main ypes o ML models a e
supe ised lea ning and unsupe ised lea ning. Supe ised lea ning models, including
Random Fo es , Suppo Vec o Machines, and Mul i-laye Pe cep on, a e
pa icula ly e ec i e in add essing classi ica ion and eg ession challenges. On he
o he hand, unsupe ised lea ning me hods a e employed o clus e ing asks. The
p esen s udy u ilized i e supe ised ML models: Random Fo es Classi ie , Decision
T ee, Mul i-laye Pe cep on, Suppo Vec o Machines, and K-Nea es Neighbo s o
ca ego ize he sui abili y o sola powe plan s in o i e classes as pe manen ly
unsui able, ma ginally no sui able, ma ginally sui able, mode a ely sui able, and
highly sui able. The classi ica ion me hods ha e demons a ed highe accu acies o
classi ica ion asks in p e ious s udies. The Models we e de eloped in a Py hon
en i onmen using he Sciki -lea n package.
Chap e 3. Da a and Me hodology
34
3.2.2.1 Random Fo es classi ie (RF)
A Random Fo es classi ie is an enhanced e sion o a decision ee whe e nume ous
unco ela ed decision ees a e combined in o a single s uc u e called a o es . I is
widely used o add ess he classi ica ion and eg ession applica ion due o i s abili y o
classi y he inpu samples o high dimensional a iables wi hou dimension educ ion
o o e i ing (Sun e al., 2023b). The selec ion o he numbe o ees o his classi ie
is a complex ask ha needs o be ca e ully conduc ed o p e en unnecessa y noises
and o e i ing o he model. The Random Fo es model used in his s udy was
de eloped wi h 1000 unco ela ed ees o ob ain he mos accu a e esul s o he
classi ica ion p oblem.
3.2.2.2 Suppo Vec o Machines (SVM)
SVM is a obus classi ie buil on he heo e ical concep s o s a is ical lea ning o
ind he op imal hype plane in a high-dimensional ea u e space o sepa a e he da a
poin s in o classes wi h he help o a ke nel unc ion (Singh, 2019). This me hod can
uniquely o e come complex non-linea ela ionships wi h he help o he ke nel
unc ion, which maps he inpu da a in o high-dimensional ea u e space (Hou e al.,
2023). In his s udy, he SVM op imiza ion was achie ed by selec ing he Gaussian
adial basis unc ion as he designa ed ke nel o ope a ions, wi h a chosen C alue o
0.2 assigned o con ol he smoo hness o he decision bounda ies.
3.2.2.3 Decision T ee (DT)
Decision ee classi ie s a e algo i hms ha use he ules o make decisions by
ecu si ely pa i ioning he inpu space in o subse s based on he mos signi ican
ea u es. These classi ie s a e easy o in e p e and hus aluable in decision-making
and classi ica ion p oblems. The s abilized and mos accu a e decision ee chosen o
he s udy has con igu ed he Spli e pa ame e 'bes ' and se he Min Impu i y Dec ease
o 0.1.
3.2.2.4 k-Nea es Neighbo s (k-NN)
k-Nea es Neighbo s is an analy ical ML me hod used o classi ica ion and
eg ession-based p oblems. This me hod calcula es he dis ance om each class o he
objec s, and he class wi h he minimum dis ance is assigned as he objec 's class
Chap e 3. Da a and Me hodology
35
(Singh, 2019). The classi ie 's accu acy was imp o ed by ine- uning pa ame e s,
including he numbe o neighbo s, sea ch me hod (g id and andom), and dis ance
me ic. In he p esen s udy, he model's pe o mance was op imized by employing he
Euclidean dis ance, 5 neighbo s, and a andom sea ch me hod.
3.2.2.5 Mul i-Laye Pe cep on (MLP)
Mul i-laye Pe cep on is a widely used a i icial neu al ne wo k o classi ica ion, da a
mining, and modeling p ocesses (Asadi & Pou hossein, 2021). The s uc u e o an
MLP model comp ises an inpu laye , an ou pu laye , and one o mo e in e media e
laye s dedica ed o ecei ing decision ac o s, ou pu ing classi ied esul s, and
pe o ming he compu a ional p ocess o he model, espec i ely (Sachi e al., 2022a).
In supe ised classi ica ion, he MLP model is ained h ough backp opaga ion,
whe ein he model's accu acy is imp o ed in each i e a i e p ocess by adjus ing he
model wi h a speci ied lea ning a e.
The MLP model u ilized in his s udy employed a Mul i-laye Pe cep on (MLP) model
wi h 4 hidden laye s, comp ising 100, 80, 60, and 40 Pe cep on, espec i ely. The
chosen ac i a ion unc ion was he ec i ie linea uni , and he model unde wen
aining wi h a maximum o 5000 i e a ions. An ea ly s opping me hod was
implemen ed o p e en model o e i ing, which moni o ed he alida ion ac ion se
a 0.2.
3.2.3 Model e alua ion, alida ion, and selec ion
Pe o mance e alua ion is an essen ial ask in ML p ojec s, which allows he
assessmen o he accu acy o p edic ions using a new da a se (Hou e al., 2023). A e
implemen ing he me hods using Py hon’s execu able code, he pe o mance o each
me hod was assessed by u ilizing con usion ma ices o de i e pe o mance me ics,
including Accu acy, P ecision, F1 Sco e, and Recei e Ope a ing Cha ac e is ic
(ROC) cu es, which calcula e he a ea unde he cu es. Finally, he model ha
achie ed he highes F1 sco e and he mos signi ican a ea unde he cu e was chosen
as he op imal one o ad ance he esea ch.
Chap e 3. Da a and Me hodology
36
3.2.3.1 Recei e Ope a ing Cha ac e is ic (ROC) cu es
The ROC cu es a e o en used o compa e he pe o mance o classi ica ion asks by
illus a ing he ac ual posi i e a e o p edic ions agains he alse posi i e a e (Sachi
e al., 2022a). The a ea unde he cu e is he quan i a i e measu e ha e alua es he
quali y o he ROC cu es. The AUC alues can ange om 0.5 o 1, 1 indica ing a
pe ec model. Since he s udy da ase con ained i e sui abili y classes as labels,
sepa a e ecei e ope a ing cha ac e is ics we e eco ded o each class o calcula e he
a eas unde he cu es.
3.2.3.2 Accu acy
The measu ing accu acy quan i ies he pe o mance o he ML models by ob aining
he a io o co ec ly classi ied pixels ( ue posi i es) o he o al numbe o pixels,
which is he sum o ue posi i es, ue nega i es, alse nega i es, and alse posi i es
(I za Alejand a e al., 2020). The accu acy o i e ML models was independen ly
compu ed o i e sui abili y classes, u ilizing he de eloped con usion ma ix. Then,
he inal accu acy alue was de i ed by calcula ing he unweigh ed a e age ac oss he
di e en land use classes.
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃
𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 ............................................ (3.2)
3.2.3.3 P ecision
P ecision is he a io be ween ue posi i es and all posi i es cap u ed by he model,
namely ue and alse posi i es (Si e el Ricka d, 2004). I was employed in he s udy
o measu e he ML model's capaci y o p edic posi i e ins ances accu a ely. Simila
o he accu acy calcula ions, he p ecision o independen classes was calcula ed o
ob ain he a e age p ecision o each ML model.
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃
𝑇𝑃+𝐹𝑃 ................................................... (3.3)
3.2.3.4 F1 sco e
The F1 Sco e is a ma ix used o measu e a model's o e all pe o mance by combining
p ecision and ecall in o a single ma ix (Sh es ha & Vanneschi, 2018). I measu es he
model's abili y o cap u e all posi i e ins ances by ge ing he ha monic mean o
Chap e 3. Da a and Me hodology
37
p ecision and ecall, whe e p ecision measu es he a io be ween ue posi i es and he
sum o ue and alse posi i es. In con as , ecall measu es he a io be ween ue
posi i es and he sum o ue posi i es and alse nega i es (Abdollahi e al., 2020).
Gi en he mul iclass classi ica ion na u e, his s udy employs he mac o-a e age F1
sco e o he assessmen .
𝐹1 𝑆𝑐𝑜𝑟𝑒 = 2∗𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛∗𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙 ........................................... (3.4)
3.2.4 Explainable AI
Explainable AI (XAI) is a concep aiming o explain he in e nal p ocess o an ML
model by p o iding use -unde s andable explana ions o he me hod, he p ocess, and
he ou pu o an ML model (Sachi e al., 2022a). In his s udy, he popula XAI
algo i hm, known as SHAP, is used o explain he RF model, which was iden i ied as
he mos accu a e among he i e selec ed models. Shapely in oduced he SHAP
algo i hm (Vajda e al., 1951) in 1951 o e alua e he con ibu ion o indi idual playe s
o a n-pe sons game whe e mo e han one playe is con ibu ing o he game. I was
subsequen ly expanded in a i icial in elligence o in e p e model p edic ions in 2017
by Sco Lundbe g (Lundbe g & Lee, 2017). The SHAP algo i hm is applied in a ious
o ms, such as T eeSHAP, DeepSHAP, and Ke nel SHAP. This s udy uses T eeSHAP,
designed explici ly o ML models based on ee s uc u es (Sachi e al., 2022b).
The SHAP algo i hm has wo explainabili y echniques: global and local explana ions,
whe e he global explana ions desc ibe he model in gene al. In con as , local
explana ions desc ibe e e y obse a ion in he model (Dallanoce, 2022). In his s udy,
he global in e p e a ions o he model we e ob ained by illus a ing he summa y plo s
o he model. These explana ions aim o map he con ibu ion o each independen
a iable in assessing he loca ional sui abili y o sola powe plan s. Then, he local
explana ions we e ob ained h ough o ce plo s, highligh ing he ac o s ha
signi ican ly con ibu ed o he p edic ion by iden i ying he e ec o each independen
a iable on classi ying an indi idual pixel in he sui abili y map.

Chap e 3. Da a and Me hodology
38
3.2.5 Sola powe plan sui abili y map
As he nex s ep o he s udy, a sui abili y map o es ablishing sola powe plan s in
he Uni ed S a es o Ame ica was gene a ed using he ained model. A e emo ing
he p o ec ed a eas, he model's p edic ions we e displayed ca og aphically as a map,
se ing as a aluable esou ce o he decision-making p ocess in he u u e. The
quan i a i e analysis o he spa ial dis ibu ion was subsequen ly ca ied ou o de i e
insigh s ega ding he pe cen ages o land pa cels and a eas sui able o mapping sola
powe plan s. In he inal s age o he me hodology, a compa a i e analysis was
conduc ed be ween he exis ing sola powe plan s classi ied acco ding o hei
e iciency alues, and hose a e ob ained h ough model p edic ions. This assessmen
aimed o iden i y disc epancies be ween he g ound u h da a and he model
p edic ions.
39
4. RESULTS AND DISCUSSION
This chap e ocuses on he isualiza ion and discussion o he ou comes de i ed om
he conduc ed s udy. The ini ial sec ions showcase he ou comes o he p e-p ocessing
s eps. The ollowing sec ion illus a es he esul s ob ained du ing he model selec ion
and accu acy assessmen . The inal wo sec ions p o ide a de ailed accoun o he
esul s ob ained o explainable AI and he inal sola sui abili y maps.
4.1 Resul s
4.1.1 Da a p e-p ocessing
A e applying a ious da a p e-p ocessing s eps, including ep ojec ion, clip, as e -
o- ec o con e sion, and esampling, Figu e 4.1 – 4.12 p esen s he inal as e laye s
o he wel e independen a iables.
Figu e 4.1: Ai empe a u e
Chap e 4. Resul s and Discussion
40
Figu e 4.4: Aspec
Figu e 4.3: Popula ion densi y
Figu e 4.2: Land use / land co e
Chap e 4. Resul s and Discussion
41
Figu e 4.7: Cloud index
Figu e 4.6: Ele a ion
Figu e 4.5: Global ho izon al i adia ion
Chap e 4. Resul s and Discussion
48
The ou comes e eal ha sola adia ion is he p ima y de e minan in sola powe
plan sui abili y modeling, con ibu ing 13% o he model. Following closely, he cloud
index and p oximi y o he ci y cen e eme ge as he second and hi d mos signi ican
ac o s, wi h in luences o 12% and 11%, espec i ely. Subsequen ly, ai empe a u e
and ele a ion sha e equal impo ance a 9% each in he model. The leas impac ul
ac o in he model is he aspec , con ibu ing only 5% o he o e all impo ance o he
RF model.
4.1.6 Sola powe plan sui abili y map
Figu e 4.16 depic s he esul ing map indica ing he sui abili y o he sola powe plan
gene a ed h ough he Random Fo es classi ica ion model. Be o e c ea ing he map,
he p edic ions o he ML model unde wen a il e ing p ocess, which was used o
assign he "Pe manen ly no sui able" class o p o ec ed a eas.
The quan i a i e examina ion o he ul ima e sui abili y map is p esen ed in Table 4.4,
indica ing ha a ound 5% o he o al land a ea in he Uni ed S a es is highly sui able
o cons uc ing sola powe plan s, ensu ing op imal ope a ional e iciency.
Addi ionally, 55% o he land is mode a ely sui able o such es ablishmen s.
Con e sely, app oxima ely 9.5% o he o al land a ea, equi alen o app oxima ely
Figu e 4.15: Weigh s o he ac o s

Chap e 4. Resul s and Discussion
49
766654 km2, is deemed no pe manen ly sui able o sola powe plan cons uc ion.
This limi a ion is p ima ily a ibu ed o en i onmen ally p o ec ed a eas ese ed
h oughou he coun y.
Class
A ea (sq. km)
Pe cen age (%)
Highly sui able
389666
4.9
Mode a ely sui able
4377279
55.07
Ma ginally sui able
2329314
29.3
Ma ginally no sui able
86039
1.08
Pe manen ly no sui able
766654
9.65
Table 4.4: Quan i a i e examina ion o he inal sui abili y map
4.1.7 Compa a i e analysis o g ound u h da a and model p edic ions
Figu e 4.17 illus a es he o e lay o exis ing powe plan s o e he sui abili y map
p oduced using he Random Fo es model. The quan i a i e analysis conduc ed o e
he o e laying map is p esen ed in , displaying he numbe o exis ing sola powe
plan s wi hin each sui abili y class o he map along wi h he ac ual numbe o
he sola powe plan s loca ed in he espec i e class ha was calcula ed using he
me hod desc ibed in he sec ion 3.2.1.
Figu e 4.16: Final sui abili y map
Chap e 4. Resul s and Discussion
50
No:
Class
G ound
u h da a
P edic ion
Map
Di e ence
Model Accu acy
1
Highly sui able
173
187
14
O e es ima e
2
Mode a ely sui able
609
383
26
Unde es ima e
3
Ma ginally sui able
2996
3006
10
O e es ima e
4
Ma ginally no sui able
939
1114
25
Unde es ima e
5
Pe manen ly no sui able
223
250
27
O e es ima e
Table 4.5: The dis ibu ion o p edic ed and exis ing sola powe plan s o e he s udy egion
The obse ed disc epancies sugges ha he p edic ions o e es ima e h ee classes and
unde es ima e wo classes. Howe e , gi en he sligh di e ences in alues, i can be
assumed ha he model pe o med well in spa ial dis ibu ion.
4.2 Discussion
The con en ional app oaches o si e sui abili y assessmen in scien i ic esea ch o en
in ol e using mul ic i e ia-based analysis me hods o iden i y he op imal loca ions
o ins alling sola powe plan s. Howe e , hese con en ional me hods ha e inhe en
limi a ions ela ed o he subjec i e selec ion o c i e ia and he ca ego iza ion o
in luencing ac o s. Such subjec i i y can in oduce biases and pose limi a ions in eal-
wo ld enewable ene gy scena ios, especially when he e is insu icien knowledge o
ma u i y in he me hodology. Ne e heless, he p oposed s udy uses ad ancemen s in
he a ailabili y o eal-wo ld digi ally o med spa ial da a, geog aphical in o ma ion
sys ems, and emo ely sensed condi ional ac o s o de elop mo e ma u e and less
Figu e 4.17: Exis ing sola powe plan s o e he sui abili y map p oduced wi h he RF model
Chap e 4. Resul s and Discussion
51
biased ML-based solu ions ha e ec i ely add ess loca ional sui abili y issues. The
pe o mance measu es o he cu en s udy, whe e h ee ou o he i e selec ed models
achie ed an accu acy exceeding 74%, a i m he sui abili y and e ec i eness o such
me hods in decision-making p ojec s.
The highes accu acy, p ecision, F1 sco e, and AUC we e achie ed using he Random
Fo es classi ie , ollowed by he decision ees, K-nea es neighbo s, and Mul i-laye
Pe cep on in he gi en s udy. These esul s align wi h p e ious indings, emphasizing
he e iciency and accu acy o he Random Fo es model o loca ional sui abili y
esea ch (Sachi e al., 2022), (Aami Shahab & M.P. Singh, 2022), (Sun e al., 2023).
Mo eo e , he esul s o he impo ance o condi ional ac o s calcula ed wi h he
Random Fo es model e eal ha he sola adia ion, cloud index, and p oximi y o he
ci y cen e o he esea ch a ea employ he mos signi ican in luences on he
selec ions. These weigh s o e aluable insigh s o choosing a loca ion o hos a sola
powe plan in an economically e icien en i onmen . The ac o s pinpoin ed as he
mos in luen ial by he model a e consis en wi h he ecommenda ions de i ed om
nume ous s udies in he ield o loca ional choice esea ch (Akkas e al., 2017),
(Youse i e al., 2018), (Ghe boudj & Ghedi a, 2016), (Husein e al., 2023).
Meanwhile, he ou comes de i ed h ough he XAI app oach ha e con ibu ed o a
deepe comp ehension o he selec ed model and he condi ioning ac o s ha
signi ican ly a ec a speci ic loca ion's classi ica ion wi hin a pa icula sui abili y
class. The global explana ions o he Random Fo es (RF) model o e ed insigh s in o
he mean in luence o each p edic ion ac oss i e sui abili y classes. Sola adia ion
exhibi ed he highes mean SHAP alue, ollowed by cloud index and ai empe a u e.
In addi ion, he local explana ions elucida ed he impac o each ac o on he model
conce ning hei assignmen o speci ic ca ego ies. Sola adia ion eme ged as he mos
in luen ial ac o o he highly sui able class, wi h lowe alues nega i ely impac ing
he class and highe alues posi i ely con ibu ing o he class. The analysis o e ed a
ho ough o e iew o each ac o , illus a ing how lowe and highe alues impac
each classi ica ion class. The p oposed s udy concludes ha he ML models a e
sui able and ha Explainable AI app oaches e ec i ely explain he selec ed ML-based
Chap e 4. Resul s and Discussion
52
model o sola powe plan sui abili y mapping a a esolu ion o 1 km2 o he
con inen al USA. Fu he , i is essen ial o acknowledge ha he cu en s udy has
p ima ily ocused on e alua ing he physical, clima ic, and cons uc ion cos - ela ed
pa ame e s a o able o he selec ion p ocess o sola powe plan si es. Howe e , o
a comp ehensi e assessmen , i is necessa y o also accoun o addi ional economic
and poli ical ac o s.
53
5. CONCLUSIONS AND RECOMMENDATION
This chap e summa izes he s udy's conclusion in alignmen wi h he p ede ined
esea ch objec i es. Addi ionally, i add esses he iden i ied limi a ions and
ecommenda ions o u u e esea ch endea o s, aiming o enhance and e ine he
s udy's ou comes.
5.1 Conclusions
The s udy's p ima y objec i e aimed o de elop an explainable AI-based ML model
o mapping he sui abili y o sola powe plan loca ions by conside ing he e iciency
o he plan as he dependen a iable o he ML models ins ead o conside ing all
exis ing plan s in he ideal loca ions. Inco po a ing e iciency in o he model in ol ed
success ul implemen a ion, whe e calcula ions we e execu ed using ene gy p oduc ion
and capaci y da a om he U.S. Ene gy In o ma ion Adminis a ion (EIA). Howe e ,
he absence o cu o alues o classi ying plan s in o sui abili y classes led o
de eloping a hypo he ical assump ion, elabo a ed in Chap e 3, sec ion 3.2.1.
Fi e ML algo i hms (Random Fo es , Decision ee, K-nea es neighbo s, mul i-laye
Pe cep on, and Suppo ec o machines) we e employed in he s udy using eal-wo ld
da a. Resul s con i med he supe io i y o he Random Fo es model o he s udy wi h
high-pe o mance alues o accu acy me ics: F1 Sco e: 0.881, P ecision: 0.881,
Accu acy: 0.881. Consequen ly, he Random Fo es algo i hm can be sugges ed as a
sui able model o he sui abili y assessmen amewo ks o sola powe plan s. The
conclusi e sui abili y map de eloped u ilizing a chosen Random Fo es indica es ha
app oxima ely 4.90% o he o al land a ea in he USA is ecommended o
cons uc ing sola powe plan s o achie e highe e iciency in ene gy p oduc ion.
Fu he mo e, 55.07% o he land is classi ied as mode a ely sui able, wi h 29.30% and
1.08% iden i ied as ma ginally sui able and ma ginally no sui able, espec i ely.

Chap e 5. Conclusions and Recommenda ions
54
These indings p o ide a p ac ical and ac ionable o e iew o decision-make s and
s akeholde s in he sola ene gy sec o , o e ing insigh s in o he mos sui able a eas
o op imal and e icien sola powe plan cons uc ion ac oss he coun y. The
conclusions d awn om add essing he es ablished esea ch ques ions in pu sui o he
p ima y objec i e a e as ollows:
1. Wha a e he key ac o s ha signi ican ly in luence he sui abili y o sola
powe plan si e selec ion p ocess?
In conclusion, examining key ac o s in luencing he sui abili y o sola powe
plan si e selec ion in ol ed an ex ensi e li e a u e e iew, encompassing
s udies beyond and wi hin he s udy a ea. This e iew iden i ied 14 ac o s ha
signi ican ly in luence he si e selec ion p ocess as Sola adia ion (SR), Cloud
index (CI), P oximi y o ci y cen e s (PC), Popula ion densi y (PD), Ele a ion
(E), P oximi y o oad ne wo k (PR), Land use and land co e (LULC),
P oximi y o g id line (PG), Ai empe a u e (AT), Wind speed (WS), Aspec
(A), Slope (S), Humidi y (HD), and Na u al disas e s (ND). Howe e , al hough
humidi y and occu ences o na u al disas e s in he a ea we e iden i ied as
signi ican , hose we e excluded om he s udy due o he da a una ailabili y.
This highligh s he impo ance o he iden i ied ac o s and unde sco es he need
o comp ehensi e da a a ailabili y o a mo e exhaus i e analysis in u u e
s udies.
2. Which ML-based classi ica ion me hod is he mos accu a e o c ea ing a
sui abili y map o iden i y po en ial sola powe si es using exis ing sola
p oduc i i y da a?
In conclusion, he in es iga ion in o ML-based classi ica ion me hods aimed a
c ea ing a sui abili y map o iden i ying po en ial sola powe si es yielded
aluable insigh s. The s udy employed i e dis inc me hods: Random Fo es ,
Decision ees, K-nea es neighbo s, Mul i-Laye Pe cep on, and Suppo
Vec o machines. Th ough igo ous e alua ion, Random Fo es eme ged as he
mos accu a e classi ica ion me hod, demons a ing supe io pe o mance in
e ms o accu acy. The selec ion o Random Fo es as he p e e ed model o
gene a ing a sui abili y map unde sco es i s e icacy in le e aging exis ing sola
Chap e 5. Conclusions and Recommenda ions
55
p oduc i i y da a o p ecise iden i ica ion o po en ial sola powe si es. This
conclusion p o ides p ac ical guidance o u u e endea o s in sola si e
selec ion, emphasizing he impo ance o employing Random o es s o
op imal accu acy and eliabili y.
3. Wha is he in luence o each ac o on he inal sui abili y map?
In conclusion, he SHAP analysis, conduc ed h ough explainable AI
echniques, highligh ed he c i ical ole o indi idual en i onmen al,
geomo phological, spa ial, and clima ic c i e ia in he spa ial decision-making
p ocess o p edic ing he sui abili y class o sola powe plan loca ions. These
ac o s we e anked in o de o impo ance, om highes o lowes , as ollows:
SR - 13%, CI - 12%, PC - 11%, AT - 9%, E - 9%, PD - 8%, WS - 7%, PR - 7%,
LULC - 7%, PG - 6%, S - 6%, and A - 5%
5.2 Limi a ions and Recommenda ions
While his s udy con ibu es aluable insigh s in o Explainable ML-based sola powe
plan selec ion me hodologies, i is essen ial o acknowledge ce ain limi a ions
inhe en in he me hodology and da a sou ces. These limi a ions may impac he
gene alizabili y and applicabili y o he indings.
1. Una ailabili y o da a:
Limi ed da a a ailabili y o humidi y and he occu ence o na u al disas e s
es ic ed hei inclusion in he analysis, emphasizing he need o
comp ehensi e da ase s in u u e s udies.
2. Limi ed li e a u e on e iciency-based classi ica ion:
Sca ce li e a u e on classi ying sola powe plan s based on e iciency
cons ained he s udy's dep h, highligh ing he impo ance o expanding
esea ch in his a ea.
3. Insu icien eco ded sola powe plan s:
The s udy aced limi a ions due o insu icien sola powe plan s wi h eco ded
p oduc ion and consump ion da a, pa icula ly o la ge-scale si e sui abili y
mapping. Collabo a i e e o s a e needed o compile a mo e ex ensi e da ase
o u u e esea ch.
Chap e 5. Conclusions and Recommenda ions
56
Fo u u e ecommenda ions, he s udy would unde sco e he impo ance o
inco po a ing sola panels' il and sun o ien a ion as in luen ial ac o s, hus
enhancing he comp ehensi e unde s anding o si e sui abili y o sola powe
plan s.
57
6. BIBLIOGRAPHIC REFERENCES
Abdollahi, A., P adhan, B., Shukla, N., Chak abo y, S., & Alam i, A. (2020). Deep
lea ning app oaches applied o emo e sensing da ase s o oad ex ac ion: A
s a e-o - he-a e iew. Remo e Sensing, 12(9).
h ps://doi.o g/10.3390/RS12091444
Akinsola, J. E. T., Awodele, O., Kuyo o, S. O., & Kasali, F. A. (2019). Pe o mance
E alua ion o Supe ised Machine Lea ning Algo i hms Using Mul i-C i e ia
Decision Making Techniques. In e na ional Con e ence on In o ma ion
Technology in Educa ion and De elopmen (ITED), Mcdm, 17–34.
h ps://www.academiain o ma ion echnology.o g/i ed2019/uploads/8135_File_0
3ITED19041 IEEE Pape Fo ma Pe o mance E alua ion o Supe ised
Machine Lea ning Algo i hms Using MCDM Techniques NEW (1).pd
Akkas, O. P., E en, M. Y., Cam, E., & Inanc, N. (2017). Op imal Si e Selec ion o a
Sola Powe Plan in he Cen al Ana olian Region o Tu key. In e na ional
Jou nal o Pho oene gy, 2017(7 June 2017).
h ps://doi.o g/10.1155/2017/7452715
Al-Ruzouq, R., Shanableh, A., Yilmaz, A. G., Id is, A. E., Mukhe jee, S., Khalil, M.
A., & Gib il, M. B. A. (2019). Dam si e sui abili y mapping and analysis using
an in eg a ed GIS and machine lea ning app oach. Wa e (Swi ze land), 11(9).
h ps://doi.o g/10.3390/w11091880
Alami Me ouni, A., Elwali Elalaoui, F., Mez hab, A., Mez hab, A., & Ghennioui, A.
(2018). La ge scale PV si es selec ion by combining GIS and Analy ical
Hie a chy P ocess. Case s udy: Eas e n Mo occo. Renewable Ene gy, 119, 863–
873. h ps://doi.o g/10.1016/j. enene.2017.10.044
Ali, R., Hussain, A., Nazi , S., Khan, S., & Khan, H. U. (2023). In elligen Decision
Suppo Sys ems—An Analysis o Machine Lea ning and Mul ic i e ia Decision-
Making Me hods. Applied Sciences, 13(22), 12426.
h ps://doi.o g/10.3390/app132212426
Almansi, K. Y., Sha i , A. R. M., Abdullah, A. F., & Ismail, S. N. S. (2021). Hospi al
si e sui abili y assessmen using h ee machine lea ning app oaches: E idence
om he gaza s ip in Pales ine. Applied Sciences (Swi ze land), 11(22), 1–22.
h ps://doi.o g/10.3390/app112211054
Asadi, M., & Pou hossein, K. (2021). Neu al ne wo k-based modelling o wind/sola
a m si ing: a case s udy o Eas -Aze baijan. In e na ional Jou nal o Sus ainable
Ene gy, 40(7), 616–637. h ps://doi.o g/10.1080/14786451.2020.1833881
Asadi, M., Pou hossein, K., Noo ollahi, Y., Ma zband, M., & Iglesias, G. (2023). A
New Decision F amewo k o Hyb id Sola and Wind Powe Plan Si e Selec ion
Using Linea Reg ession Modeling Based on GIS-AHP. Sus ainabili y
(Swi ze land), 15(10). h ps://doi.o g/10.3390/su15108359
Annexes A
64
Figu e A.3: ROC cu e o mode a ely sui able class
Figu e A.4: ROC cu e o ma ginally sui able class

Annexes A
65
Figu e A.5: ROC cu e o highly sui able class
66
Annexes B
Figu e B.2: Signi icance o condi ioning ac o s o he p edic ions o ma ginally no sui able class
Figu e B.1: Signi icance o condi ioning ac o s o he p edic ions o pe manen ly no sui able class
Annexes B
67
Figu e B.3: Signi icance o condi ioning ac o s o he p edic ions o ma ginally sui able class
Figu e B. 4: Signi icance o condi ioning ac o s o he p edic ions o highly sui able class
68
Sui abili y mapping o sola powe plan s using an
explainable AI-based app oach
2024
Mawanane Hewa Rasanka Mangala De Sil a
Guia pa a a o ma ação de eses Ve são 4.0 Janei o 2006
69