Application of HKELM Model Based on Improved Seahorse Optimizer in Reservoir Dissolved Oxygen Prediction

Abstract

As an important part of environmental science and water resources management, water quality prediction is of great importance. In order to improve the efficiency and accuracy of predicting dissolved oxygen (DO) at the outlet of a reservoir, this paper proposes an improved Seahorse Optimizer to enhance the hybrid kernel extreme learning machine model for water quality prediction. Firstly, the circle chaotic map is used to initialize the hippocampus population to improve the diversity and quality of the population, and then the sine and cosine strategy is used to replace the predation behavior of the hippocampus to improve the global search ability. Finally, the lens imaging reverse learning strategy is used to expand the search range and prevent it from falling into the local optimal solution. By introducing two kernel functions, a global kernel function (Poly) and a local kernel function (RBF), a new hybrid kernel function extreme learning machine is formed by linearly combining these two kernel functions. The parameters of this HKELM are optimized with the improved Seahorse Optimizer, and the water quality prediction model of CZTSHO-HKELM is constructed. The simulation results show that the operating efficiency and prediction accuracy of the model are better than those of the ELM, CZTSHO-ELM, CZTSHO-KELM, and SHO-HKELM models, with the correlation coefficients increased by 5.5%, 3.3%, 3.4%, and 7.4%, respectively. The dissolved oxygen prediction curve is close to the actual dissolved oxygen change, which can better meet the requirements of reservoir water quality prediction. The above method can be applied to further accurately predict the water quality of the reservoir.