Forecasting DO of the river-type reservoirs using input variable selection and machine learning techniques - taking Shuikou reservoir in the Minjiang River as an example

Abstract

Dissolved oxygen (DO) plays a significant role in maintaining the health of aquatic ecosystems. In this study, we propose a model that incorporates multiple machine learning methods for predicting DO. Firstly, the maximum information coefficient (MIC) was utilized to identify the key drivers of DO. Afterward, the particle swarm optimization (PSO) algorithm was used to enhance the traditional support vector regression (SVR) model, optimizing the penalty factor (c) and the width of the Gaussian kernel function (g). This resulted in the development of a MIC-PSO-SVR-based DO prediction model. As a case study, we analyzed three points (G1, G2, and Z1) located in the mainstem and tributaries of the Shuikou Reservoir in Fujian, China. The original dataset, encompassing various time scales and sample sizes, underwent reclassification. Key factors influencing DO were determined by calculating the MIC values between DO and each monitoring factor. The main findings of this study are as follows: (1) By assessing the correlation between candidate factors and DO, the MIC effectively eliminated irrelevant variables with low correlation, thereby reducing the dataset size. Furthermore, it was observed that the fluctuations in MIC values for each variable stabilized when the sample size exceeded 4000. (2) The model’s performance exhibited improvement with the reduced dataset. For instance, the mean absolute error (MAE) and root mean square error (RMSE) of the hybrid MIC-PSO-SVR model decreased by approximately 66% and 49%, respectively, whereas the R2 and Nash-Sutcliffe efficiency (NSE) are as high as 0.98 and 0.95, showing superior performance compared to the unreduced PSO-SVR model. (3) More importantly, the model successfully predicted sudden hypoxia events in the Shuikou reservoir area from October 11 to November 8, 2021. Additionally, the MIC-PSO-SVR model accurately captured DO changes in the point G2 in front of the Shuikou dam during a hypoxic event in the Shuikou reservoir. The prediction errors during hypoxia were as low as 0.23 mg·L-1 and 0.31 mg·L-1 for 1 h and 24 h, respectively.