Search

To optimize the water quality index (WQI) assessment model, this study upgraded the parameter weight values and aggregation functions. We determined the combined weights based on machine learning and game theory to improve the accuracy of the models, and proposed new aggregation functions to reduce the uncertainty of the model. A new water quality assessment system was established, and took the Chaobai River Basin as a case study. To optimize the weight, two combined weights were established based on game theory. The weight CW AE was combined by the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). The weight CW AL was combined by AHP and machine learning (LightGBM). CW AL was judged to be an optimal composite weight by comparing the coefficient of variation (CV) values and the Kaiser-Meyer-Olkin (KMO) extracted values. To reduce the uncertainty of the model, we proposed two aggregation functions, the Sinusoidal Weighted Mean (SWM) and the Log-weighted Quadratic Mean (LQM). The three water quality assessment models (WQI S , WQI L and WQI W ) were established based on the optimal weights besides. All three models had good reliability. Both WQI S and WQI W models had low eclipsing problems (25.49% and 18.63%). The accuracy of the models was ranked as WQI S > WQI W > WQI L . The uncertainty of WQIs (0.000) in assessing poor water quality was low, and so was WQI W (0.259) in assessing good water quality. Overall, the WQI S model was recommended for assessing poor water quality and the WQI W model was recommended for assessing good water quality. The assessment results of WQI S showed that the Chaobai River Basin was "slightly polluted", and the water quality upstream was better than that downstream. TN was the main pollutant in the basin, and there was slight pollution with COD Mn , COD Cr , BOD 5 , etc. There was little metal contamination, only a few months exceeded Class I. The model established in this study can provide a reference for the same type work of water quality assessment. The assessment results can provide a scientific basis for the protection of the regional water environment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

To improve the rationality of weight allocation and weight proportion of different periods in the process of water quality assessment, the comprehensive water quality identification index (CWQII) model was optimized in this study. A new improved comprehensive water quality identification index (ICWQII G ) model based on game theory was established to combine subjective weight and objective weight. Based on ICWQII G , an improved comprehensive water quality identification index (ICWQII P ) model with phased period combination weights was established to determine determined the weight proportion of phased periods was established. In this study, CWQII, ICWQII G , and ICWQII P were used to evaluate the water quality of seventeen sites in Taihu Lake in 2020. The models selected nine water quality parameters and six water quality indicators. The assessment results of water quality classification were between "slightly polluted" and "moderately polluted". The pollution level on the east bank was lower than that on the west bank and north bank. Furthermore, it was also affected by seasonal change, water quality was worse in January and February but better in October and November. The mean value of I wq calculated by CWQII, ICWQII G , and ICWQII P were 2.405, 2.833, and 3.000, respectively. The compared results showed that the ICWQII G method can more representative identify the location of polluted water than CWQII. Moreover, the ICWQII P method calculation results not only retained the representative polluted water samples in the ICWQII G method but can also identify more pollution sites and worse polluted water bodies. Both ICWQII G and ICWQII P had high reliability and accuracy in assessment results, and ICWQII P was more accurate under sufficient data conditions. This study can offer a scientific basis for local water resource management in Taihu Lake, while simultaneously proposing a science-based and valid methodology for the assessment of other similar water bodies., (Copyright © 2022 Elsevier Inc. All rights reserved.)