Abstract
Abstract A hybrid model based on the mind evolutionary algorithm is proposed to predict hourly water demand. In the hybrid model, hourly water demand data are first reconstructed to generate appropriate samples so as to represent the characteristics of time series effectively. Then, the mind evolutionary algorithm is integrated into a back propagation neural network (BPNN) to improve prediction performance. To investigate the application potential of the proposed model in hourly water demand forecasting, real hourly water demand data were applied to evaluate its prediction performance. In addition, the performance of the proposed model was compared with a traditional BPNN model and another hybrid model where the genetic algorithm (GA) is used as an optimization algorithm for BPNN. The results show that the proposed model has a satisfactory prediction performance in hourly water demand forecasting. On the whole, the proposed model outperforms all other models involved in the comparisons in both prediction accuracy and stability. These findings suggest that the proposed model can be a novel and effective tool for hourly water demand forecasting.
Highlights
Water demand forecasting is of great importance for water resources management and planning in water distribution systems
There are some review papers on the models for water demand forecasting in which various models are discussed (Donkor et al 2014; Ghalehkhondabi et al 2017). These models can be categorized into two main groups: conventional models (e.g. auto-regressive moving average (ARMA) and auto-regressive integrated moving average (ARIMA)), and artificial intelligence (AI)-based models (e.g. artificial neural networks (ANNs))
They often fail to capture the nonlinear relationships in time series data, which limits their applications in water demand forecasting
Summary
Water demand forecasting is of great importance for water resources management and planning in water distribution systems. There are some review papers on the models for water demand forecasting in which various models are discussed (Donkor et al 2014; Ghalehkhondabi et al 2017) These models can be categorized into two main groups: conventional models (e.g. auto-regressive moving average (ARMA) and auto-regressive integrated moving average (ARIMA)), and artificial intelligence (AI)-based models (e.g. artificial neural networks (ANNs)). Conventional models have the advantages of simple structure and low computation load These models can yield good forecasts when time series data are under a stationary condition. They often fail to capture the nonlinear relationships in time series data, which limits their applications in water demand forecasting. Banjac et al (2015) used ANNs with an adaptive
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