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.

Through bringing nutrient-rich subsurface water to the surface, the artificial upwelling technology is applied to increase the primary marine productivity which could be assessed by Chlorophyll a concentration. Chlorophyll a concentration may vary with different water physical properties. Therefore, it is necessary to study the relationship between Chlorophyll a concentration and other water physical parameters. To ensure the accuracy of predicting the concentration of Chlorophyll a, we develop several models based on wavelet neural network (WNN). In this study, we build up a three-layer basic wavelet neural network followed by three improved wavelet neural networks, which are namely genetic algorithm-based wavelet neural network (GA-WNN), particle swarm optimization-based wavelet neural network (PSO-WNN), and genetic algorithm & particle swarm optimization-based wavelet neural network (GAPSO-WNN). The experimental data were collected from Qiandao Lake, China. The performances of the proposed models are compared based on four evaluation parameters, i.e., R-square, root mean square error (RMSE), mean of error (ME), and distance (D). The modeling results show that the wavelet neural network can achieve a certain extent of accuracy in modeling the relationships between Chlorophyll a concentration and the five input parameters (salinity, depth, temperature, pH, and dissolved oxygen).