Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

ABSTRACT: Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R²). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised

SUBMITTER: Yang M 

PROVIDER: S-EPMC11557846 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature