Abstract
This study proposes a hybrid approach for accurately predicting water demand by integrating socio-economic variables, such as population and GDP (per capita), with climatic variables, including temperature and precipitation. The prediction model utilizes an Extreme Learning Machine (ELM), effectively capturing the dynamic relationships between the input variables and water demand. The Improved Ant Nesting Algorithm is employed to fine-tune the weights and biases to optimize the network's performance. To evaluate the predictive accuracy of the model, a comprehensive dataset consisting of socio-economic and climatic factors is utilized for training and testing purposes. Performance metrics, namely Root Mean Square Error (RMSE) and Correlation Coefficients (R(2)), are employed as evaluation criteria. The results demonstrate that the hybrid approach achieves accurate water supply predictions, showcasing its potential to contribute significantly to effective water resource management and decision-making processes. Based on the results, IANA-ELM is considered the best model due to its high R(2) values. Specifically, in the training data, the R(2) values are 0.693 for population, 0.624 for GDP per capita, 0.607 for temperature, and 0.708 for rainfall. Similarly, in the test data, the R(2) values are 0.672 for population, 0.608 for GDP per capita, 0.592 for temperature, and 0.708 for rainfall. This integrated approach provides a robust tool for policymakers, water utility companies, and researchers in the field of water managements, enabling them to make informed decisions based on accurate predictions of water demand.