2025· International Journal of Emerging Trends in Multidisciplinary Research· 0 citations
TL;DR
An AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, cloud-edge computing, and predictive analytics for real-time pipeline monitoring and early leak detection is proposed, supporting sustainable, reliable, and intelligent water resource management.
Abstract
Rapid urbanization, industrialization, and climate change have intensified water scarcity, creating a need for intelligent water distribution systems. Traditional leak detection methods rely on manual inspections and threshold-based monitoring, resulting in delayed detection, high water losses, increased maintenance costs, and infrastructure damage. This study proposes an AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, cloud-edge computing, and predictive analytics for real-time pipeline monitoring and early leak detection. The framework collects data from pressure, flow, acoustic, and water quality sensors, applying machine learning algorithms to identify hydraulic anomalies and predict leakage probabilities. It also incorporates digital twins and hydraulic simulation models to improve adaptability under varying operational conditions. Mathematical models evaluate leak probability, sensor reliability, and system performance, enabling proactive maintenance and informed decision-making. The proposed architecture enhances detection accuracy, minimizes false alarms, reduces non-revenue water losses and operational costs, and improves infrastructure resilience, supporting sustainable, reliable, and intelligent water resource management.
An AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, edge-cloud computing, and predictive analytics for real-time leak detection and proactive maintenance and provides a scalable and sustainable solution for intelligent water resource management.
Iyengar P. K.· International Journal of Eme...· 0 citations
Smart water distribution systems play a vital role in addressing modern water management challenges caused by urbanization, population growth, industrial expansion, and climate change. Traditional water supply networks often suffer from high water losses, inefficient monitoring, delayed fault detection, and increased operational costs due to manual management practices. To overcome these limitations, smart water management integrates real-time sensor networks, Internet of Things (IoT) technologies, wireless communication, cloud computing, and data analytics. Advanced sensors continuously monitor critical parameters such as water pressure, flow rate, water quality, leakage, temperature, pH, and contamination levels across pipelines, reservoirs, treatment plants, and consumer endpoints. This study examines the architecture, communication mechanisms, sensing technologies, and optimization techniques used in smart water distribution systems. The proposed framework employs layered deployment of pressure, flow, and water-quality sensors combined with cloud-based analytics and predictive control algorithms. Wireless communication technologies such as ZigBee, LoRaWAN, GSM, and Wi-Fi enable efficient data transmission across distributed infrastructure. The system supports real-time leakage detection, pressure regulation, contamination monitoring, predictive maintenance, and energy-efficient pump scheduling. Simulation results demonstrate significant improvements in leakage detection accuracy, operational efficiency, water conservation, energy savings, and infrastructure reliability compared to conventional distribution networks. The findings indicate that smart water distribution systems powered by real-time sensor networks can transform traditional water utilities into intelligent, adaptive, and sustainable platforms, supporting resilient water management and future smart city development.
James Carter, Patricia Hall· International Journal of Mod...· 0 citations
Water leakage in urban water distribution networks (WDNs) poses significant challenges for sustainable resource management and infrastructure reliability. Traditional detection methods are often reactive and difficult to scale in modern sensor-rich environments. This paper proposes a hybrid data-driven framework for early leak detection that integrates physics-informed simulation with machine learning and explainable analytics. A region-aware EPANET-style simulator is developed to generate realistic hydraulic data under varying demand patterns, environmental conditions, and pressure-dependent leak scenarios. To enhance generalizability, the synthetic dataset is combined with a BATADAL-inspired benchmark, enabling both in-domain and cross-domain evaluation. A feature engineering pipeline is introduced to capture temporal, spatial, and hydraulic relationships, expanding raw sensor signals into a high-dimensional representation. Six machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression, Isolation Forest, and a PCA-Based Autoencoder, are systematically evaluated under constrained false-positive requirements. The results show that tree-based ensemble models achieve strong detection performance while maintaining low false-alarm rates (FPR ≤ 0.05). Importantly, cross-domain experiments demonstrate that models trained on simulated data retain competitive performance when applied to benchmark datasets, indicating robust transferability. Finally, explainability analysis reveals that pressure-based temporal statistics and spatial gradients are key indicators of leakage, providing interpretable insights for system monitoring. The proposed framework offers a scalable and generalizable approach for intelligent leak detection in modern water distribution systems.
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Parvathy Krishna V, G. S, Sahala Mehrin et al.· International Journal of Tec...· 0 citations
Artificial intelligence (AI) is rapidly transforming water management by enabling data-driven prediction, intelligent monitoring, optimization, and decision support across water-resource systems. This review critically examines the emerging applications of AI in smart water management, with particular emphasis on the transition from conventional monitoring and prediction toward integrated decision-making and autonomous system control. The review synthesizes recent developments in machine learning, deep learning, Internet of Things (IoT)-based systems, reinforcement learning, explainable artificial intelligence, and emerging agentic AI approaches applied to water resources, water-quality monitoring, groundwater management, hydrological forecasting, reservoir operation, wastewater treatment, water- demand prediction, and smart water infrastructure. The major benefits of AI include improved prediction accuracy, real-time monitoring, early detection of water-quality deterioration, optimization of operational processes, and enhanced decision support. However, widespread implementation remains constrained by data quality and availability, model interpretability, computational requirements, cybersecurity risks, infrastructure limitations, high implementation costs, and regulatory and governance challenges. Particular attention is given to the emerging transition from AI- assisted decision support toward autonomous water-system control, where AI models can increasingly connect sensing, prediction, decision-making, and physical actions. The review further identifies key research gaps related to trustworthy and explainable AI, integration of heterogeneous data sources, physics-informed machine learning, human-in-the-loop decision-making, and equitable deployment in resource-constrained water systems. Finally, a conceptual framework is proposed linking sensing, AI-based prediction, decision support, autonomous control, and continuous feedback, providing a pathway toward resilient, intelligent, and sustainable water management systems.
K. Pragadeeshwaran· International Journal Of Rec...· 0 citations
Floods associated with intense rainfall, rapid water-level rise, and sudden dam discharge pose serious risks to communities, infrastructure, agriculture, and the environment. Conventional flood alert systems generally rely on predefined water-level thresholds and therefore respond only after a critical condition has occurred. This paper presents an intelligent Internet of Things (IoT) framework that combines real-time water-level monitoring with machine-learning-based flood-risk classification for smart dam safety. The proposed system is developed from an existing IoT flood-alert prototype using a NodeMCU ESP8266, multi-level water sensors, LCD display, buzzer, and Wi-Fi communication. The original threshold-based monitoring mechanism is extended with a Random Forest (RF) classifier to provide intelligent classification of water conditions into Normal, Warning, and Critical states. The sensor layer continuously acquires water-level information, while the ESP8266 performs local processing and communicates the observations through Wi-Fi. A structured dataset containing sensor-state features and corresponding flood-risk labels is used for model development. The Random Forest approach is selected because of its ability to model nonlinear relationships, handle categorical and numerical features, and provide computationally efficient inference. The resulting framework combines local safety alerts with remote IoT monitoring and predictive analytics. The proposed architecture provides a practical pathway for transforming a low-cost threshold-based flood alert prototype into an intelligent early-warning system. The reported prototype observations confirm correct detection of low, medium, and highwater conditions and immediate activation of the danger alarm. Quantitative machine-learning performance should be reported only after validation using a sufficiently large measured dataset collected from the deployed system.
Unknown authors· International Research Journ...· 0 citations
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