WATER DEMAND FORECASTING USING MACHINE LEARNING APPROACH
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Date
2025-10-01
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Abstract
The increasing challenges related to water security, exacerbated by rapid urbanization, population growth, and climate variability, necessitate accurate and reliable forecasting methodologies to support sustainable water resources planning. This study explores water demand forecasting in Ha-Foso, Lesotho, by evaluating three machine learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). Utilizing time series data sets covering meteorological inputs (2012-2022), population, and water consumption records (2017-2024), the study assesses the influence of climatic and demographic variables, specifically precipitation, maximum and minimum temperatures, and population, on domestic water consumption.
The research first used MLR to assess the influence of population, maximum temperature, minimum temperature, precipitation, and other factors on water demand. Subsequently, the study evaluated the predictive performance of MLR, SVR, and ANN models. Performance was evaluated using performance metrics, including the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).
The regression analysis consistently identified population as the only statistically significant predictor of water demand (p<0.001), while climatic variables showed no significant influence during the study period. In the comparative evaluation, the SVR demonstrated the highest accuracy and generalization capacity, outperforming ANN and MLR, with the least error metrics in both the training phase and the testing phase. The 2-year forecast highlighted the distinct behaviours of each model, with the SVR and ANN models providing more moderate growth projections compared to the steep, linear increase predicted by the MLR model.
This study presents the potential of machine learning, particularly SVR and ANN, in addressing the intricate, non-linear relationships inherent in water demand forecasts, delivering precise and actionable water demand forecasts for peri-urban settings in Lesotho. The findings suggest the adoption of advanced architectures and the incorporation of socio-demographic variables to strengthen predictive capacity. The outputs are expected to support utility companies such as Water and Sewerage Company (WASCO) in strategic planning, conservation, and infrastructure investment decisions.