Simulation of chlorophyll-a concentration in Donghu Lake based on GA-ELM and multiple water quality indexes

Xiaodong Tang; Mutao Huang

doi:10.1117/12.2626448

21 December 2021 Simulation of chlorophyll-a concentration in Donghu Lake based on GA-ELM and multiple water quality indexes

Xiaodong Tang, Mutao Huang

Author Affiliations +

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121561K (2021) https://doi.org/10.1117/12.2626448
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Simulation of chlorophyll-a concentration is an important method for monitoring lake eutrophication. In recent years, plenty of achievements have made in the study on chlorophyll-a concentration inversion model and water quality numerical model. However, the application of these two kinds of models is limited in lakes water with small data sample. In this paper, building non-mechanism model based on the relationship between chlorophyll-a concentration and multiple water quality indexes is proved to be feasible, at the same time, linear regression (LR) model, extreme learning machine (ELM) model, and ELM optimized by genetic algorithm (GA-ELM) model were built to simulate chlorophyll-a concentration. The simulation results show that the simulation effect of ELM model is better than that of LR model, and GA-ELM model works best. In addition, genetic algorithm (GA) effectively improves the simulation effect of ELM, R² of GA-ELM model increased by 0.1570, RMSE decreased by 7.12 μg/L, MAPE decreased by 0.7117 when compared with ELM model. The study provides a simple and effective method to simulate chlorophyll-a concentration, which can be applied for practical monitoring eutrophication in Donghu Lake.

Citation Download Citation

Xiaodong Tang and Mutao Huang "Simulation of chlorophyll-a concentration in Donghu Lake based on GA-ELM and multiple water quality indexes", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121561K (21 December 2021); https://doi.org/10.1117/12.2626448

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KEYWORDS

Data modeling

Computer simulations

Genetic algorithms

Neural networks

Error analysis

Performance modeling

Statistical modeling

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