ELM Forecasted Model of Ammonia Nitrogen in Lake Taihu Combined with Adaboost and Particle Swarm Optimization Algorithms

Abstract

AbstractTo understand the change trend of Lake Taihu’s water quality indexes in advance and prevent the occurrence of pollution incidents such as blue algae, the ammonia nitrogen index forecasted model was studied by taking the water quality monitoring data of Nanquan in Wuxi City as an example. The chromaticity, odor and taste, pH, nitrite nitrogen, chloride, dissolved oxygen, and algae were determined as input variables by implementing linear regression analysis through SPSS software to establish the Extreme Learning Machine (ELM) model for forecasting ammonia nitrogen index. Aiming at the problem of ELM randomly generating weights and thresholds resulting in low forecast accuracy, the Particle Swarm Optimization (PSO) algorithm was adopted to optimize ELM parameters. In order to solve the situation that the PSO algorithm is easy to fall into local optimum, the Adaboost algorithm was introduced to train weak ELM predictors, which were later combined into a strong predictor for the forecast. The results represent that the Adaboost-PSO-ELM model’s forecast coefficient of determination (\({R}^{2}\)) and root mean square error (\(RMSE\)) for ammonia nitrogen are 0.955 mg/L and 0.023 mg/L, respectively. Compared with the two models of ELM and PSO-ELM, the forecast accuracy is significantly improved. Therefore, the ELM model based on Adaboost and PSO algorithms is feasible for forecasting water quality index of ammonia nitrogen in Lake Taihu, providing new ideas for accurate forecast in water quality aspect.KeywordsLake TaihuAmmonia nitrogenAdaboost-PSO-ELM