Abstract
Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.
Highlights
Security of municipal water is fundamental to gain a sustainable environment in modern cities, especially under the impact of global warming and socio-economic variables
To the best of the authors’ knowledge, the present study explores a novel methodology for the first time: the effects of climate change on the monthly stochastic pattern of urban water demand
The potential of novel coupled data pre-processing and automated machine learning for monthly stochastic urban water coupled demand prediction based on several climatic factors was In this study, the potential of novel data pre-processing and automated machine investigated
Summary
Security of municipal water is fundamental to gain a sustainable environment in modern cities, especially under the impact of global warming and socio-economic variables. Most cities are located close to freshwater sources to ensure the prosperity of both industry and agriculture. Water 2020, 12, 2692 mentioned reasons, freshwater scarcity is a classic problem for policymakers [1,2]. Economic Forum confirmed that water scarcity is one of the largest international risks because of the limited amount of accessible freshwater (approximately 0.014% of the total amount of water on Earth). Climate change, water pollution and poor management of freshwater sources are other major factors that contribute to water scarcity. Climate change increases water demand which increases the pressure on the urban water system, especially during periods of water shortage
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