A Context-Aware, Predictive Decision Support System for Water Quality and Infrastructure Management at Asa Dam, Nigeria

305 Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 p ISSN: 2635-3342; e ISSN: 2635-3350 Original Research Article A Context-Aware, Predictive Decision Support System for Water Quality and Infrastructure Management at Asa Dam, Nigeria *1Aderemi, I.A., 2Benson, K.P., 3Audu, M.F., 4Audu, I.B., 5Tijani, S.A. and 6Eleso, S.A. 1Water Resources and Environmental Engineering, Faculty of Engineering Technology, University of Ilorin, Ilorin, Nigeria. 2Department of Science Laboratory Technology (Geology Technology Option), University of Jos, Jos, Nigeria. 3Department of Chemical Science, University of Turin, Italy. 4Department of Agricultural Extension and Rural Development, University of Ilorin, Nigeria. 5Department of Chemistry, University of Ilorin, Nigeria. 6Department of Plant Science and Biotechnology, Federal University, Oye Ekiti, Nigeria. *[email protected]; [email protected]; [email protected]; [email protected]; tjbolan[email protected]; [email protected] http://doi.org/10.5281/zenodo.18060979 ARTICLE INFORMATION ABSTRACT Article history: Received 08 Jul. 2025 Revised 21 Sep. 2025 Accepted 03 Oct. 2025 Available online 30 Dec. 2025 In rapidly urbanising regions such as Ilorin, Nigeria, water utilities face mounting pressures from ageing infrastructure, ecological degradation, and limited operational intelligence, compounded by the absence of realtime monitoring and predictive capabilities. This study develops a conceptual, context-aware Decision Support System (DSS) framework to strengthen the resilience and efficiency of the Asa Dam Water Treatment Plant, Ilorin. Guided by Design Science Research Methodology, the framework synthesises global advances in digital water management and the specific institutional and environmental realities of sub-Saharan Africa. The architecture integrates Internet of Things (IoT) sensors, Supervisory Control and Data Acquisition (SCADA) systems, Geographic Information Systems (GIS), and predictive algorithms, including Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks, to enable real-time water quality monitoring, anomaly detection, demand forecasting, and ecological risk zoning. Distinctively, the framework embeds explainable artificial intelligence (XAI), stakeholder engagement mechanisms, and hybrid cloud–edge deployment to ensure transparency, accountability, and adaptability in resource-constrained settings. Although empirical implementation is beyond the present scope, the study contributes a technically justified, ethically grounded, and locally adaptable blueprint that complements ongoing revitalisation efforts in Nigeria’s water sector. It further provides a transferable foundation for piloting and institutional integration in comparable utilities across sub-Saharan Africa, aligning with the objectives of Sustainable Development Goal 6. © 2025 RJEES. All rights reserved. Keywords: Decision support system Smart water management IoT LSTM GIS Explainable AI Water governance Sub-Saharan Africa 1. INTRODUCTION Water quality monitoring and sustainable water resource management in environmentally fragile and rapidly urbanizing regions such as Ilorin, Nigeria, have grown increasingly complex. The Asa Dam 306 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 Water Treatment Plant, Ilorin, responsible for supplying water for domestic, industrial, and agricultural use, must continuously track vital parameters like turbidity, pH, dissolved oxygen, and contaminants to protect public health, maintain ecological balance, and comply with regulatory standards (Ali et al., 2020; Touhari et al., 2022). However, the region’s hydro-environmental challenges, including fluctuating rainfall patterns, agricultural runoff, unregulated fishing, and invasive species such as water hyacinth, complicate these monitoring tasks (Tadesse & Dinka, 2023). Traditional water management systems, which rely heavily on manual sampling and laboratory analysis, lack the temporal sensitivity to respond to rapid shifts in water quality, often resulting in delayed interventions and increased operational risks (Rühling Tagliari et al., 2024; Sholagberu et al., 2025). Compounding these environmental pressures is the projected demographic expansion of Ilorin, where the population is expected to grow from 1.03 million in 2017 to 3.4 million by 2050. This surge will escalate daily water demand from 156,000 m³ in 2020 to an estimated 512,000 m³ by 2050, representing a more than threefold increase (State Ministry of Water Resources, 2023). Despite its strategic importance, the Asa Dam faces significant distribution losses, up to 36,000 m³ daily, due to aging infrastructure and leakage. These inefficiencies compromise the dam’s resilience and its ability to meet future demand. As climate variability, ecological degradation, and population growth converge, the challenges facing water management in Ilorin are no longer solely technical; they are also socio-political and institutional. This underscores the need for proactive, real-time, and data-driven solutions tailored to the region’s unique context (Nwankwo Constance Obiuto et al., 2024; Hussein et al., 2023). Globally, Decision Support Systems (DSS) have evolved from static, model-driven tools toward datadriven and AI-enabled platforms that integrate telemetry, geospatial intelligence, and predictive analytics to support operational decisions under uncertainty. Contemporary applications combine IoT/SCADA sensing, edge–cloud computing for low-latency analytics, and machine learning such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), tree ensembles to forecast demand, detect anomalies, and optimize process control (e.g., chemical dosing, selective withdrawal) in near real time (Shahra et al., 2024). Recent advances in digital twins and GIS-based multi-criteria decision frameworks extend DSS capabilities beyond treatment plants to basin-wide monitoring, enabling spatial risk zoning, dynamic optimization, and stakeholder transparency (Zhao et al., 2025). In parallel, short-term water-demand forecasting with deep learning models has become a cornerstone for capacity planning, energy efficiency, and chemical optimization in utilities (Drogkoula et al., 2023). Despite this progress, contextualization for Sub-Saharan African utilities remains under-addressed. Most DSS frameworks assume data-rich, well-instrumented, and stable environments, conditions rarely found in lowand middle-income countries. Where bandwidth is limited, staff turnover is frequent, and policies evolve, deployments that rely on centralized cloud or opaque “black-box” analytics can falter. Thus, two persistent research gaps emerge: (1) a need for locally adaptable, low-latency architectures that operate reliably under intermittent connectivity/power; and (2), explainability and governance mechanisms that build operator trust, support accountability, and align with ethical AI use in public services. This research article addresses these gaps by developing a conceptual, context-aware DSS framework tailored to the Asa Dam Water Treatment Plant. Guided by the Design Science Research Methodology (DSRM) (Peffers et al., 2007),, the study proposes and theoretically justifies a modular artefact that integrates IoT/SCADA sensing, predictive models, GIS-based risk zoning, and Explainable AI (XAI) features to enhance adoption and accountability. Unlike conventional DSS studies, this framework explicitly incorporates stakeholder co-design, low-bandwidth cloud–edge deployment, and ethical safeguards, thereby providing a blueprint for empirical piloting and institutional scaling in sub-Saharan African contexts. The study is guided by three objectives: 1. To develop an integrated, predictive DSS architecture for real-time water quality monitoring and management at Asa Dam. 2. To contextualize the architecture for local hydrological, infrastructural, and institutional conditions. 3. To design a stakeholder-responsive roadmap that embeds transparency, accountability, and institutional sustainability. 307 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 By doing so, the paper contributes to the theoretical literature on adaptive DSS design in fragile water governance systems, the methodological literature on Design Science applications in environmental management, and the practical discourse on sustainable water governance, aligning with Nigeria’s Water Resources Master Plan and SDG 6. 2. METHODOLOGY The development of the proposed Decision Support System (DSS) followed the Design Science Research Methodology (DSRM) (Peffers et al., 2007), which provides an iterative and structured framework for designing information systems artefacts. DSRM progresses through cycles of problem identification, objective definition, design, demonstration, and theoretical justification. This methodology is particularly well-suited to studies in under-resourced contexts, where immediate empirical validation may be impractical but rigorous design logic remains essential. Applying DSRM, the present study was able to systematically translate the operational realities of the Asa Dam Water Treatment Plant into a conceptual framework that integrates real-time monitoring, predictive analytics, geospatial risk zoning, and stakeholder-centred governance. The framework design was informed by a comprehensive review of global literature, international DSS applications, and technical reports. Special attention was paid to emerging technologies such as Internet of Things (IoT) sensors, Supervisory Control and Data Acquisition (SCADA) systems, Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) models, all of which are increasingly being applied in resilient water management systems. The design process also accounted for the infrastructural and institutional challenges characteristic of utilities in sub-Saharan Africa, such as intermittent power supply, weak data management capacity, and volatile policy environments. The resulting DSS is therefore modular and multi-layered, integrating sensing technologies, predictive and optimization algorithms, spatial intelligence, and adaptive decision modules. While the study is conceptual and does not include empirical implementation, it provides a theoretically informed and contextually grounded framework to guide future pilot projects, stakeholder validation, and scaling in comparable African water utilities. 2.1. Study Area Asa Dam (Figure 1), located on the Asa River about 5 km south of Ilorin's city center in Kwara State, Nigeria, is a primary water source for Ilorin and its surrounding communities. Constructed by Julius Berger Nigeria PLC and inaugurated in 1978, the dam supports various functions, including irrigation, fisheries, livestock management, and recreational activities. The dam measures 597 meters in length and 27 meters in height, comprising three main sections: a 400-meter earth fill dam, a 150-meter concrete gravity dam, and a 160meter lateral earth dam. It has a storage capacity of 44 million cubic meters and a spillway designed to handle 1,300 m³/s. Initially intended for hydropower generation, this purpose has not been achieved. The Asa Dam Water Treatment Plant, located at 8.457° N latitude and 4.548° E longitude, is essential for providing potable water to the region. Despite its strategic importance, the plant faces significant challenges related to deteriorating infrastructure, environmental contamination, and rising demand due to urbanization. 2.2. Current Practices and Challenges at Asa Dam The water treatment process at Asa Dam generally follows conventional procedures of aeration, coagulation, flocculation, filtration, and chlorination, ensuring that water supplied meets Nigerian Industrial Standards and WHO guidelines. Yet despite these established processes, the plant faces persistent challenges that compromise operational efficiency. Industrial effluents, agricultural runoff, and domestic waste continually pollute the Asa River, complicating treatment and increasing costs. In addition, seasonal infestations of water hyacinth obstruct intake systems, raise biological oxygen demand (BOD), and trigger eutrophication, especially during the rainy season. The ageing infrastructure of the plant further exacerbates inefficiencies, with leakage and distribution losses estimated at thousands of cubic metres daily. The reliance on manual sampling and laboratory-based testing also introduces significant delays in detecting water quality changes, leaving the system vulnerable to rapid shifts in turbidity or microbial contamination. Recognizing these challenges, the Nigerian government and international partners such as the World Bank and the ACReSAL initiative have launched revitalisation programmes to enhance the resilience of Asa Dam. These interventions provide financial resources, technical expertise, and institutional support aimed at addressing both infrastructure and ecological issues. However, while such programmes are vital, their effectiveness remains constrained by the absence of integrated, real-time decision-making tools. Without digital 308 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 augmentation, responses to water quality deterioration remain reactive, and opportunities for predictive and preventive management are missed. Figure1: Map of Ilorin showing Asa Dam (Ifabiyi et al., 2016) 2.3. Justification for DSS Implementation Given the multifaceted challenges facing the Asa Dam Water Treatment Plant—including infrastructure degradation, pollution, and rising demand—a data-driven, integratedDSS is essential for sustainable water management. By incorporating IoT sensors, SCADA systems, and machine learning models such as ANNs and LSTM networks, the DSS can enable real-time monitoring, predictive analytics, and automated decisionmaking. Empirical studies show such systems can reduce chemical consumption by 20–30% and improve anomaly response times by over 40% (Jonoski & Seid, 2016). Additionally, GIS-based risk zoning and predictive maintenance capabilities can optimise interventions, extend infrastructure lifespan, and minimise service disruptions. Aligned with Nigeria’s National Water Resources Master Plan and Sustainable Development Goal 6—particularly indicators 6.3.2 and 6.5.1—the proposed DSS supports adaptive, transparent, and sustainable water governance in line with ACReSAL's objectives. 3. RESULTS AND DISCUSSION 3.1. DSS Architecture Layers The proposed DSS for Asa Dam is structured as a multi-layered and modular architecture that integrates sensing, analytics, geospatial intelligence, and stakeholder participation. The architecture is designed for resilience under infrastructural constraints and scalability across comparable utilities in sub-Saharan Africa. The specific layers of the architecture, their core functions, enabling technologies, and global benchmarks are summarized in Table 1. 3.2. Intelligent Architecture of the Proposed DSS for the Asa Dam The DSS architecture for the Asa Dam Water Treatment Plant is a modular, scalable framework that integrates real-time environmental monitoring, predictive analytics, spatial intelligence, and stakeholderresponsive design. Developed in response to the complex challenges of water quality and distribution in Ilorin, Nigeria, including hydrological variability, ageing infrastructure, and ecological degradation, the system relies on IoT sensors and a SCADA network to continuously collect key metrics such as turbidity, dissolved oxygen, and microbial loads. These data streams are processed through centralized or decentralized storage systems, with built-in redundancy and advanced techniques like Kalman filtering and Gaussian process imputation to ensure continuity and reliability even under variable network conditions. 309 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 Table 1: Layers of the proposed DSS architecture for Asa Dam Layer Functions Key technologies Global benchmarks Sensing & data acquisition Continuous monitoring of turbidity, pH, dissolved oxygen, microbial loads IoT sensors, SCADA Similar layers in Singapore Smart Water Grid (Public Utilities Board Singapore, 2016) Data processing & storage Filtering, redundancy, data integrity Kalman filtering, Gaussian imputation Edge–cloud frameworks in European utilities (Shahra et al., 2024) Predictive analytics Demand forecasting, anomaly detection, treatment optimization ANN, LSTM, Model Predictive Control (MPC) Deep learning models applied in Canadian and European utilities (Drogkoula et al., 2023) Spatial intelligence Mapping pollution hotspots, ecological risk zoning GIS, MCDA GIS-based DSS in Kenya and South Africa (Matheri et al., 2022) Decision & governance interface Operator dashboards, mobile access, transparency XAI (SHAP), audit trails, SMS alerts XAI in DSS for healthcare and environmental services (Floridi et al., 2018) To support proactive decision-making, the DSS incorporates AI and machine learning models such as LSTM networks andANNs to predict demand fluctuations, detect anomalies, and optimise treatment operations. A GIS-based risk zoning module provides spatial insights to manage pollution hotspots linked to human activities, while a dual-interface design—comprising a control room dashboard and mobile access for field personnel—ensures accessibility and participatory governance. Additionally, the architecture supports hybrid cloud-edge deployment for low-latency performance in bandwidth-limited environments, making it both technically robust and contextually adaptive. Beyond operational efficiency, the DSS serves as a platform for institutional learning and long-term resilience in water governance. Figure 2 depicts the novel DSS architecture needed in Asa Dam water treatment facility to enhance the system. Figure 2: Novel DSS architecture for Asa Dam water treatment 3.3. Predictive Control and Optimization in Treatment Operations The proposed DSS for Asa Dam is strategically designed to integrate real-time sensor data, predictive machine learning (ML) models, and GIS-based asset mapping to address critical stressors such as turbidity spikes and algal blooms, which are often triggered by rainfall or nutrient surges. Drawing on insights from (Fameso et al., 2024), the system mirrors the successful regional applications of locally calibrated DSS 310 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 frameworks that effectively respond to climate variability and governance challenges. At Asa Dam, this transition from reactive to forecast-driven operations marks a pivotal shift, enabling early detection and response to quality deterioration, thus ensuring continuous access to safe water. The system utilizes Model Predictive Control (MPC) to simulate daily operations and optimize dosing schedules, leveraging real-time and historical data, along with weather inputs. As demonstrated in Canadian utilities, integration with SCADA systems has led to a 25% reduction in energy and chemical consumption (Zhang et al., 2012); similar outcomes are anticipated at Asa Dam, particularly during high turbidity seasons. To enhance transparency and foster operator trust, the DSS also incorporates Explainable AI (XAI) methodologies, notably Shapley Additive exPlanations (SHAP), allowing operators to interpret how input variables, such as turbidity, inflow velocity, or temperature, contribute to predictions. This contrasts with conventional black-box models by offering visibility into algorithmic reasoning, which is vital for accountability and institutional adoption. (Floridi et al., 2018) emphasize that explainability and fairness are crucial for the ethical use of AI in public services, particularly in sensitive sectors such as water governance. Accordingly, the system is designed with built-in explainers, data privacy safeguards, audit trails, and optin mechanisms for stakeholder feedback. These features not only enhance technical reliability but also ensure that the DSS supports equitable, transparent, and participatory water treatment governance at Asa Dam. 3.4. Managing Water Hyacinth using DSS-Driven AI Models Water hyacinth (Eichhornia crassipes) is one of the most invasive aquatic species, threatening the operational efficiency and ecological balance of freshwater systems, including Asa Dam. These fast-growing plants create dense mats that obstruct raw water intakes, disrupt hydrological flow, and significantly increase BOD, leading to eutrophication and diminished water quality, particularly in nutrient-rich tropical waters (Kebedew et al., 2023; Dersseh et al., 2022). Such infestations hinder aquatic life and compromise the dam’s safe potable water supply to surrounding communities. To manage these challenges proactively, the DSS developed for Asa Dam integrates AI-driven forecasting tools with environmental data. The system is trained on historical biomass records, nutrient concentrations, water temperature, and seasonal trends, enabling it to predict bloom cycles and identify infestation hotspots. This predictive capability supports timely interventions that mitigate treatment disruptions and reduce operational costs. Machine learning techniques, including ANNs and Random Forest classifiers, have been successfully used in similar ecological contexts to analyse seasonal growth patterns and inform removal strategies (RodríguezAlonso et al., 2024; Pratiwi & Andhikawati, 2021). These models can forecast the spatial distribution of biomass with over 85% accuracy, facilitating targeted mechanical, chemical, or biological control efforts. For Asa Dam, such precision ensures that interventions are practical and resource-efficient. In addition to forecasting growth, the DSS incorporates spatial classification tools, such as discriminant analysis and realtime mapping, to monitor water hyacinth coverage and trigger early warning alerts. These insights enable plant operators to anticipate potential blockages and maintain uninterrupted operations. Moreover, integrating these capabilities within a broader DSS framework enhances the sustainability of dam management practices while promoting ecological restoration and long-term system resilience (Udugamasuriyage et al., 2024; Martins et al., 2024). 3.5. Pollution and Risk Zoning for Human Activities Unregulated activities such as fishing, bathing, and washing around Asa Dam contribute significantly to water pollution by introducing contaminants like coliforms, organic waste, phosphates, and microplastics, especially in stagnant or shallow zones (Lin et al., 2017; Lin et al., 2020). These pollutants elevate pathogen and nutrient levels, increasing treatment complexity, operational costs, and health risks. To address this, the proposed DSS integrates IoT-enabled water quality sensors with GIS-based spatial analysis to monitor key parameters, such as turbidity and dissolved oxygen, in real-time, allowing for the early detection of contamination threats (Imen et al., 2018). Similar DSS applications in Kenya have shown that such zoning approaches can reduce response time to contamination events by up to 17% (Matheri et al., 2022). At Asa Dam, this GIS-enhanced system identifies areas prone to pollution. It supports targeted interventions, such as regulating access, creating designated fishing zones, and adjusting water points based on observed pollution trends (Mannina et al., 2019). The zoning models are informed by participatory mapping that incorporates local behavioural practices, ensuring policies are both practical and culturally appropriate. The DSS also facilitates stakeholder engagement by providing actionable insights for community awareness 311 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 campaigns on sustainable water practices (Ntalaperas et al., 2022). With interactive dashboards and automated alerts, operators can rapidly respond to pollution events, minimising treatment disruptions and promoting long-term environmental stewardship (Booth et al., 2011). Figure 3 illustrates the cyclic integration of GIS-based zoning, real-time pollution tracking, and targeted mitigation within the proposed DSS framework. Figure 3: Integrated DSS Cycle for Pollution Hotspot Identification and Mitigation at Asa Dam 3.6. Water Demand Forecasting and Infrastructure Planning Forecasting future water demand is crucial for effective water resource planning, particularly in rapidly urbanizing cities like Ilorin, where growing populations and climate variability exert mounting pressure on existing infrastructure. To address this, the proposed DSS for the Asa Dam Water Treatment Plant integrates forecasting tools that combine socio-demographic data, hydrological trends, and network capacity metrics. At the core of this system are LSTM neural networks—deep learning models adept at analyzing time-series data. These models simulate future water demand across two key planning horizons, 2030 and 2050, allowing stakeholders to assess whether current reservoirs, pipelines, and booster stations can accommodate projected loads. This foresight enables decision-makers to prioritize infrastructure upgrades and avoid costly delays or service disruptions. Similar DSS applications in European cities, as reported by (Drogkoula et al., 2023), have guided capital investment sequencing, enabling utilities to determine the optimal timing for expanding treatment facilities or building new pumping stations. In the context of Asa Dam, such predictive capabilities are particularly valuable in a resource-constrained environment. The DSS supports strategic resilience and financial sustainability by identifying local investment bottlenecks and enabling planners to focus on highimpact, evidence-based interventions that align with long-term demand scenarios. 3.7. Stakeholder Integration and Institutional Readiness The successful implementation of a DSS at Asa Dam relies heavily on early and sustained stakeholder engagement, involving engineers, plant operators, regulators, and community representatives (Figure. 4). Engaging these groups from the design stage ensures the system reflects both technical requirements and local realities, increasing usability and adoption (Marto et al., 2019). The co-design process has yielded practical features, including SMS alerts, local language dashboards, and mobile-friendly interfaces. To support effective use, capacity-building initiatives, beginning with a skills assessment at the Kwara State Water Corporation, will equip users with the necessary technical proficiency. However, long-term adoption also depends on addressing institutional risks such as policy shifts, leadership turnover, and funding inconsistencies (Menzel et al., 2012). To promote resilience and accountability, the DSS will be embedded within adaptive governance structures. This includes cross-agency coordination, audit mechanisms, and institutional memory tools to ensure continuity beyond project cycles(Akhmouch & Clavreul, 2016). Building a culture of data-driven decision-making is essential, as emphasized by utilities (Rohit R Dixit, 2024). Trust is reinforced through transparency, participatory workshops, and real-time feedback loops (Asogwa et al., 2024; Kovacs Burns et al., 2014). Ethical standards such as opt-in data use, anonymization, 312 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 and local language consent prompts will guide all community-facing interfaces, ensuring the DSS supports both effective water governance and public trust. Figure 4: Stakeholder-centric integration model for sustainable DSS implementation at Asa Dam 3.8. Summary of Anticipated Outcomes The Asa Dam Water Treatment Plant's proposed DSS is expected to deliver significant operational, environmental, and institutional benefits. The DSS aims to enhance technical performance and transparency in decision-making across the water treatment cycle by leveraging real-time data, predictive algorithms, geospatial tools, and stakeholder-centered design. These outcomes are informed by global benchmarks, validated case studies, and locally collected stakeholder feedback, ensuring both practical feasibility and strategic relevance. Table 2 highlights the anticipated outcomes of the application of DSS for the Asa dam water treatment facility. Table 2: Projected outcomes of DSS implementation at Asa Dam vs global benchmarks Outcome Anticipated Asa Dam impact Global benchmark Reference Chemical use optimization 20–30% reduction 25% reduction in Canadian utilities Zhang et al., 2012 Response time to anomalies 2× faster detection and intervention 40–50% faster in European DSS pilots Matheri et al., 2022 Compliance with zoning policies 30% higher compliance 25–35% increase in Kenyan case study Fameso et al., 2024 Demand forecasting Reliable projections for 2030 & 2050 Accurate urban forecasts in European cities Drogkoula et al., 2023 Infrastructure resilience Extended asset lifespan, fewer breakdowns 20–40% reduction in failures Baarimah et al., 2024 These outcomes reflect the DSS's capacity to transcend isolated improvements and contribute to system-wide transformation. Operationally, the system introduces automation and intelligence into key workflows. Environmentally, it enhances monitoring and remediation, ensuring regulatory compliance and ecological protection. Institutionally, it empowers operators and fosters stakeholder trust, crucial for sustained engagement and system longevity. 4. CONCLUSION Effectively managing water resources in rapidly urbanising and ecologically fragile regions such as Ilorin demands a shift from reactive, manual practices to proactive, data-driven approaches. This study developed a context-aware Decision Support System (DSS) framework for the Asa Dam Water Treatment Plant that 313 I.A. Adebayo and K.P. Benson / Nigerian Research Journal of Engineering and Environmental Sciences 10(2) 2025 pp. 305-315 responds directly to challenges of deteriorating water quality, infrastructure limitations, and fragmented stakeholder coordination. Guided by Design Science Research Methodology, the framework synthesises global DSS innovations and contextual realities in Nigeria into a modular, hybrid cloud–edge architecture integrating IoT, SCADA, GIS, and machine learning algorithms, including ANN and LSTM models. By enabling real-time monitoring, anomaly detection, demand forecasting, and ecological risk zoning, the proposed DSS marks a significant advancement in operational resilience and efficiency for utilities operating under resource constraints. Its integration of explainable AI, participatory design features, and governance safeguards distinguishes it from conventional DSS frameworks, ensuring inclusivity, transparency, and longterm institutional sustainability. The system’s adaptability to local contexts, through mobile dashboards, SMS alerts, and multilingual interfaces, further enhances its potential for effective deployment in subSaharan Africa. While the present study is conceptual and requires empirical validation through piloting and cost–benefit analysis, it offers a technically justified, ethically grounded, and transferable blueprint for digital transformation in water governance. The contribution is therefore threefold: theoretically, it extends designscience applications in environmental management; methodologically, it demonstrates the integration of predictive analytics, GIS, and XAI in a unified DSS; and practically, it provides a roadmap for sustainable water governance that aligns with Nigeria’s National Water Resources Master Plan and Sustainable Development Goal 6. Taken together, these contributions affirm the strategic relevance and replicability of context-sensitive DSS architectures for advancing water security and climate resilience in developing regions. 5. 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