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 demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (�C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression