Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-proc...

Full description

Saved in:
Bibliographic Details
Published in:Water (Basel) 2020-07, Vol.12 (7), p.1885
Main Authors: Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Bugharbee, Hussein, Olier, Ivan, Hashim, Khalid S., Gharghan, Sadik Kamel, Kot, Patryk, Al-Khaddar, Rafid
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Cities
Climate change
Demand
Engineering
Freshwater resources
Historical account
Information processing
Literature reviews
Neural networks
Optimization techniques
Population growth
Prediction models
Principal components analysis
Search algorithms
Security
Socioeconomic factors
Studies
Water consumption
Water demand
Water security
Water shortages
Water supply
Water utilities
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth.
ISSN:2073-4441
2073-4441
DOI:10.3390/w12071885