An improved whale optimization algorithm for forecasting water resources demand

Abstract

Water demand forecasting can promote the rational use of water resources and alleviate the pressure on water demand. By analyzing the use of water resources, this paper establishes three models of water demand forecasting, logarithmic model, linear and exponential combination model and linear, exponential and logarithmic hybrid models. In order to accurately estimate the demand for water resources, an improved whale optimization algorithm based on social learning and wavelet mutation strategy is proposed. The new algorithm designs a new linear incremental probability, which increases the possibility of global search of the algorithm. Based on the social learning principle, the social ranking and social influence are used to construct the social network for the individual, and the adaptive neighborhood learning strategy based on the network relationship is established to achieve the exchange and sharing of information between groups. The Morlet wavelet mutation mechanism is integrated to realize the dynamic adjustment of the mutation space, which enhances the ability of the algorithm to escape from local optimization. The latest CEC2017 benchmark functions confirms the superiority of the proposed algorithm. The water consumption from 2004 to 2016 in Shaanxi Province of China is used for the experiment. The results show that the performance of the proposed algorithm for solving the three water resources forecasting model is better in comparison to other algorithms. The prediction accuracy is as high as 99.68%, which verified the validity of the model and the practicality of the proposed algorithm.