Application of Extreme Learning Machine for Predicting Chlorophyll‐a Concentration Inartificial Upwelling Processes
Yan Ding Wei, Haocai Huang, Bin Chen, Bofu Zheng, Yihong Wang · Mathematical Problems in Engineering · 2019
Artificial upwelling, artificially pumping up nutrient‐rich ocean waters from deep to surface, is increasingly applied to stimulating phytoplankton activity. As a proxy for the amount of phytoplankton present in the ocean, the concentration of chlorophyll a (chl-a) may be influenced by water physical factors altered in artificial upwelling processes. However, the accuracy and convenience of measuring chl-a are limited by present technologies and equipment. Our research intends to study the correlations between chl-a concentration and five water physical factors, i.e., salinity, temperature, depth, dissolved oxygen (DO), and pH, possibly affected by artificial upwelling. In this paper, seven models are presented to predict chl-a concentration, respectively. Two of them are based on traditional regression algorithms, i.e., multiple linear regression (MLR) and multivariate quadratic regression (MQR), while five are based on intelligent algorithms, i.e., backpropagation‐neural network (BP‐NN), extreme learning machine (ELM), genetic algorithm‐ELM (GA‐ELM), particle swarm optimization‐ELM (PSO‐ELM), and ant colony optimization‐ELM (ACO‐ELM). These models provide a quick prediction to study the concentration of chl-a. With the experimental data collected from Xinanjiang Experiment Station in China, the results show that chl-a concentration has a strong correlation with salinity, temperature, DO, and pH in the process of artificial upwelling and PSO‐ELM has the best overall prediction ability.