Urban river ammonia nitrogen prediction model based on improved whale optimization support vector regression mixed synchronous compression wavelet transform

Abstract

Accurate prediction of ammonia nitrogen (NH3–N) in water was crucial to efficient and reasonable judgment of water quality and environmental conditions. A model named SCWT-IWOA-SVR was proposed in this study by combining synchronous compressed wavelet transform (SCWT), improved whale optimization algorithm (IWOA) and support vector regression (SVR). Firstly, the SCWT algorithm was used to de-noise the original data, overcome the nonlinearity of the data, and explore the variation rule of the water quality index time series. Statistical methods and Pearson correlation analysis were then used to select features. IWOA was introduced to optimize the hyperparameters in SVR kernel function, tent mapping was used to randomly initialize the location of the whales, making them more evenly distributed, combined wheel selection mechanism was used to select more representative prey, which leading to improve the accuracy of forecasts results. The results indicated that the SCWT-IWOA-SVR model, which incorporates synchronous compression wavelet transform and the improved whale optimization algorithm, exhibit a better prediction effect compared with SVR, WOA-SVR, WOA-SVR, and Random Forest (RF) models. The results indicated that the model has a good prediction performance of low error and high generalization (RMSE = 0.055, MAE = 0.015, R2 = 0.989), the RMSE <0.2 and R2 > 0.915 of the proposed SCWT-IWOA-SVR model in three days advance predicted. SCWT-IWOA-SVR model can provide a scientific basis for the comprehensive treatment and decision-making of water pollutants in the watershed.