Abstract

Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The proposed methodology utilizes historical water consumption data, meteorological variables, such as temperature, rainfall, and humidity, and real-time sensor data collected through IoT devices. Three predictive models – Multiple Linear Regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN) – are developed and evaluated using standard performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The findings reveal that the Artificial Neural Network (ANN) surpasses the other models by delivering superior predictive accuracy and stronger generalization performance. The integration of predictive analytics with real-time monitoring enables efficient water allocation, early detection of system anomalies, and reduction in non-revenue water losses. The proposed framework demonstrates significant potential for supporting sustainable and intelligent water resource management in smart cities.

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1. Jain A, Varshney AK, Joshi UC. Water demand forecasting using ANN. Water Resour Manage. 2001;15:299–321.
2. Herrera M, et al. Urban water demand forecasting. J Hydrol. 2010;387:141–150.
3. Adamowski J, Karapataki C. ANN vs regression models. J Hydrol Eng. 2010;15:729–743.
4. Shabani S, et al. Intelligent modeling of water demand. Water Resour Manage. 2018;32:123–138.
5. Zhang Q, et al. Random forest for water demand. Sustainability. 2019;11:1–15.
6. Yaseen ZM, et al. AI models in hydrology. J Hydrol. 2020;590:125130.
7. Lee S, et al. Smart water grid systems. Water. 2015;7:1–20.
8. Rao S, et al. IoT-based water monitoring. Int J Eng Technol. 2019;8:45–50.

Abstract

Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The proposed methodology utilizes historical water consumption data, meteorological variables, such as temperature, rainfall, and humidity, and real-time sensor data collected through IoT devices. Three predictive models – Multiple Linear Regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN) – are developed and evaluated using standard performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The findings reveal that the Artificial Neural Network (ANN) surpasses the other models by delivering superior predictive accuracy and stronger generalization performance. The integration of predictive analytics with real-time monitoring enables efficient water allocation, early detection of system anomalies, and reduction in non-revenue water losses. The proposed framework demonstrates significant potential for supporting sustainable and intelligent water resource management in smart cities.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Jain A, Varshney AK, Joshi UC. Water demand forecasting using ANN. Water Resour Manage. 2001;15:299–321.
2. Herrera M, et al. Urban water demand forecasting. J Hydrol. 2010;387:141–150.
3. Adamowski J, Karapataki C. ANN vs regression models. J Hydrol Eng. 2010;15:729–743.
4. Shabani S, et al. Intelligent modeling of water demand. Water Resour Manage. 2018;32:123–138.
5. Zhang Q, et al. Random forest for water demand. Sustainability. 2019;11:1–15.
6. Yaseen ZM, et al. AI models in hydrology. J Hydrol. 2020;590:125130.
7. Lee S, et al. Smart water grid systems. Water. 2015;7:1–20.
8. Rao S, et al. IoT-based water monitoring. Int J Eng Technol. 2019;8:45–50.