Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

• Main Authors: Zubaidi S.L., Kumar P., Al-Bugharbee H., Ahmed A.N., Ridha H.M., Mo K.H., El-Shafie A.
• Other Authors: 57201458677
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Subjects: Adaptive guided differential evolution algorithm; Eleven learning algorithms; Hybrid model; Monthly water demand; Australia; Melbourne; Victoria [Australia]; Discrete wavelet transforms; Evaporation; Evapotranspiration; Evolutionary algorithms; Mean square error; Meteorology; Optimization; Principal component analysis; Water supply; Weather forecasting; Demand prediction; Differential evolution algorithms; Eleven learning algorithm; Extreme weather conditions; Water demand; accuracy assessment; adaptive management; algorithm; artificial neural network; climate conditions; database; extreme event; prediction; water demand; water use; Learning algorithms

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water dema...