Wavelet Neural Network for Modeling Chlorophyll a Concentration Affected by Artificial Upwelling

Abstract

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).

Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

“…Compared with BPNN and SVM, ELM has several salient advantages: easier to use, faster learning speed, stronger generalization ability, the least human intervention [50,51]. ELM has the properties of simple parameter selection, not easy to fall into local optimal value, short training time, good generalization ability, and it can be effectively applied to the prediction of chlorophylla concentration in inland water [52][53][54].…”