A feature extraction method based on the entropy-minimal description length principle and GBDT for common surface water pollution identification

Abstract

To effectively prevent river water pollution, water quality monitoring is necessary. However, existing methods for water quality assessment are limited in terms of the characterization of water quality conditions, and few researchers have been able to focus on feature extraction methods relative to water pollution identification, unable to obtain accurate water pollution source information. Thus, this study proposed a feature extraction method based on the entropy-minimal description length principle and gradient boosting decision tree (GBDT) algorithm for identifying the type of surface water pollution in consideration of the distribution characteristics and intrinsic association of conventional water quality indicators. To improve the robustness to noise, we constructed the coarse-grained discretization features of each water quality index based on information entropy. And the nonlinear correlation between water quality indexes and pollution classes was excavated by the GBDT algorithm, which was utilized to acquire tree transformed features. Water samples collected by a southern city Environmental Monitoring Center were used to test the performance of the proposed algorithm. Experimental results demonstrate that features extracted by the proposed method are more effective than the water quality indicators without feature engineering and features extracted by the principal component analysis algorithm.

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

“…These models are implemented to achieve the predictive control of effluent quality such as biological oxygen demand (BOD), chemical oxygen demand (COD), and nutrient concentration. According to previous studies [33][34][35][36][37], a dynamic kernel extreme learning machine was proposed, including 170 samples and eight variables, to predict the COD proportion of industrial wastewater, and achieved a 10-fold cross-validation R 2 of 0.708 [38]. Alavi et al [39] proposed a novel computing algorithm that integrates an intelligent optimization algorithm with a KELM for the prediction of inlet COD concentrations in WWTPs.…”

Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model

“…The GBDT improves the capacity of the decision tree by reducing the residuals generated during the training procedure [22,23]. It has been widely applied in social science research [24][25][26][27][28] and gradually introduced into the field of natural science [1][2][3][4][5][6][7][29][30][31][32][33][34][35]. The GBDT exhibits much better performance in the retrieval of water depth compared with the single-band, dual-band, and BP neural network models [36].…”