Evolutionary artificial intelligence model via cooperation search algorithm and extreme learning machine for multiple scales nonstationary hydrological time series prediction

Abstract

Reliable and stable hydrological prediction plays a vitally crucial role in the scientific operation of water resources system. As a famous artificial intelligence method for hydrological forecasting, extreme learning machine (ELM) has the virtues of fast training efficiency and strong generalization performance but is easily trapped into local optima because the preset computation parameters often remain unchanged in the learning process. In order to overcome this shortcoming, a practical evolutionary artificial intelligence model is developed for multiple scales nonstationary hydrological time series prediction. In the proposed method, an emerging evolutionary method called cooperation search algorithm (CSA) is used to search for the optimal input-hidden weights and hidden biases of the ELM model for the first time. The proposed method is used to forecast the runoff time series of three real-world hydrological stations in China. The experimental results show that the CSA approach can effectively determine satisfying network parameters of the ELM model, while our method can produce better results than the traditional ELM method in terms of all the performance evaluation indexes. Taking 1-step-ahead runoff forecasting at station B as an example, our method betters the ELM method with 15.76% and 42.35% improvements in both root mean squared error and mean absolute percentage error at the testing phase. Thus, a novel multiscale nonstationary hydrological prediction tool is developed to support the decision-making of water resource system.