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.

中文翻译：

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受气候变化和人口增长影响的城市的城市用水需求预测：豪登省案例研究

市政供水系统的适当管理对于维持城市和支持社会的水安全至关重要。对于自来水公司的管理者和政策制定者来说，城市用水量估算一直是一项具有挑战性的任务。本文采用了一种新颖的方法，包括数据预处理和人工神经网络 (ANN)，其中使用回溯搜索算法 (BSA-ANN) 进行优化，以估计与先前用水量相关的月需水量。选择了南非豪登省 2007 年至 2016 年期间每月用水量的历史数据来创建和评估该方法。在创建预测模型之前，数据预处理技术在提高数据质量方面发挥了至关重要的作用。BSA-ANN 模型产生了最好的结果，均方根误差和效率系数分别为 0.0099 兆升和 0.979。此外，事实证明，基于误差范围，它比 Crow 搜索算法 (CSA-ANN) 更有效和可靠。总体而言，本文提出了混合模型 BSA-ANN 的新应用，该模型可成功用于高精度预测用水需求，在一个受气候变化和人口增长影响严重的城市中。