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.

中文翻译：

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具有粘菌算法的混合人工神经网络模型：一种预测城市随机需水量的新方法

由于气候因素的随机模式导致用水需求突然增加导致不确定性，因此基于气候变化的城市用水需求预测对自来水公司来说始终具有挑战性。为此，首先采用了一种新的组合方法，包括数据预处理技术，通过使用经验模式分解和通过容差识别最佳模型输入来分解水和气候因素的时间序列，以避免多重共线性。其次，人工神经网络（ANN）模型通过最新的粘液霉菌算法（SMA-ANN）进行优化，以预测城市月需水量随机信号的中期。16 年来的十个气候因素被用来模拟需水量的随机信号。结果表明，SMA 优于基于误差尺度的多节优化器和回溯搜索算法。基于统计标准的范围，混合模型 SMA-ANN 的性能优于 ANN（独立）。通常，该方法产生准确的结果，确定系数为 0.9，平均绝对相对误差为 0.001。这项研究可以帮助当地的水资源管理者有效地管理现有的水资源系统并规划扩建以适应不断增长的用水需求。