Strategic Environmental Assessment for Municipal Water Demand Based on Climate Change
type: CreativeWork
description: Accurate urban water demand forecasting plays a key role in the planning and design of municipal water supply infrastructure. The reliable prediction of water demand is challenging for water companies, specifically when considering the implications of climate change (Zubaidi et al., 2018). Several studies have documented that weather variables drive water consumption in the short-term, and it enhances the accuracy of the prediction model when it is combined with socio-economic factors. However, the impact of climate change on the municipal water demand has yet to be challenged. To surmount this challenge, more research work is needed to accurately estimate the required quantity of water with increasing water demands. Recently, Artificial Neural Networks (ANNs) have been found to be an innovative approach to predict water demand. This PhD study aims to develop a novel methodology to forecast the impact of climate change on municipal water demands for a long-term time series based on the baseline period 1980-2010. It should be highlighted that, based on our knowledge, this is the first study of substantial duration, based on data collected from 1980-2010, which focuses on the associations between monthly climate change and municipal water consumption. A new approach is therefore proposed to quantifying municipal water demands through the assessment of climatic factors, using a combination of a Singular Spectrum Analysis (SSA) technique, three hybrid computational intelligence algorithms and an ANN model. These hybrid algorithms include a Lightning Search Algorithm (LSA-ANN), a Gravitational Search Algorithm (GSA-ANN) and Particle Swarm Optimisation (PSO-ANN). The SSA technique is adopted to decompose the time series of water consumption and climate variables to detect the stochastic signal for each time series. In the same context, the hybrid algorithms are used to find the best value of learning rate coefficient and the number of neurons in both hidden layers of the …
abstract: Accurate urban water demand forecasting plays a key role in the planning and design of municipal water supply infrastructure. The reliable prediction of water demand is challenging for water companies, specifically when considering the implications of climate change (Zubaidi et al., 2018). Several studies have documented that weather variables drive water consumption in the short-term, and it enhances the accuracy of the prediction model when it is combined with socio-economic factors. However, the impact of climate change on the municipal water demand has yet to be challenged. To surmount this challenge, more research work is needed to accurately estimate the required quantity of water with increasing water demands. Recently, Artificial Neural Networks (ANNs) have been found to be an innovative approach to predict water demand. This PhD study aims to develop a novel methodology to forecast the impact of climate change on municipal water demands for a long-term time series based on the baseline period 1980-2010. It should be highlighted that, based on our knowledge, this is the first study of substantial duration, based on data collected from 1980-2010, which focuses on the associations between monthly climate change and municipal water consumption. A new approach is therefore proposed to quantifying municipal water demands through the assessment of climatic factors, using a combination of a Singular Spectrum Analysis (SSA) technique, three hybrid computational intelligence algorithms and an ANN model. These hybrid algorithms include a Lightning Search Algorithm (LSA-ANN), a Gravitational Search Algorithm (GSA-ANN) and Particle Swarm Optimisation (PSO-ANN). The SSA technique is adopted to decompose the time series of water consumption and climate variables to detect the stochastic signal for each time series. In the same context, the hybrid algorithms are used to find the best value of learning rate coefficient and the number of neurons in both hidden layers of the …
author: Zubaidi, SLF
keywords: GE Environmental Sciences, TD Environmental technology. Sanitary engineering
date published: 2018-10
date modified: 2026-07-24T06:30:25Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University

PropertyValue
property ID: DOI
value: 10.24377/researchonline.ljmu.ac.uk.00009582