Optimization of water quality index models using machine learning approaches

Ding, Fei; Zhang, Wenjie; Cao, Shaohua; Hao, Shilong; Chen, Liang‐Yao; Xie, Xing; Li, Wenpan; Jiang, Mingcen

doi:10.1016/j.watres.2023.120337

Cited by 54 publications

(7 citation statements)

References 39 publications

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“…These findings were consistent with other studies that have assessed the quality of water for different purposes using machine learning techniques [ 44 , 45 , [52] , [53] , [54] , [55] , [56] ]. However, Ding et al [ 25 ] in a study to optimize the water quality index using machine learning around the Haihe River Basin in China found that out of 178 samples evaluated the WQI in the area was classified as excellent, good, and poor quality had areas of 5.39%, 87.25%, and 7.35%. These findings were also consistent with this current study as major water quality class was found to be good.…”

Section: Resultsmentioning

confidence: 99%

“…A new and upcoming technique for groundwater prediction is the use of machine learning techniques. For groundwater mapping, machine learning algorithms like random forest have been utilized continually [ [25] , [26] , [27] , [28] , [29] , [30] ]. The current study aims to use machine learning to predict groundwater quality in Ghana's Nabogo Basin.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

Apogba,

Anornu,

Koon

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

Apogba,

Anornu,

Koon

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The common weight calculation methods are divided into subjective, objective, and combination weights. Combination weighting is a common method; two or three kinds of subjective and objective weights are combined to obtain the comprehensive weight, which can reduce the error caused by a single method to a certain extent (Ding et al, 2022a;Ding et al, 2022b;Ding et al, 2023). Based on the discussion in the introduction, the entropy weight and criteria importance through inter-criteria correlation (CRITIC) methods are applied to represent the subjective and objective factors, and the combination weights are obtained using game theory (Zhou and Yang, 2007).…”

Section: The Combination Weighting Methodsmentioning

confidence: 99%

The application of the game theory combination weighting-normal cloud model to the quality evaluation of surrounding rocks

Zhao,

Shao,

Yang

et al. 2024

Front. Earth Sci.

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The status of surrounding rocks dramatically influences the safety of construction workers, so the quality assessment of surrounding rocks has great significance. The uniaxial saturated compressive strength of rock (X1), the quality index of surrounding rock (X2), the frictional coefficient of the structural surface (X3), the joint spacing (X4), the state of groundwater(X5), and the integrity coefficient (X6) are selected as the initial evaluation index. Then, the game theory combination weighting-normal cloud model is introduced. Second, the certainty degree matrix of each index is established, and the weight coefficients of assessment indexes are determined based on the game theory combination weighting method. Finally, the quality level of surrounding rocks is judged. Compared with the traditional methods, the proposed model solves the fuzziness and randomness of different indexes, improves the reliability of the assessment process, and enhances the predictive accuracy of assessment results. In addition, it can provide a solution scheme for the evaluation indicators, which are difficult to quantify, and reduce the influence of human factors. The results obtained from the suggested model are consistent with the current specification. Its accuracy approaches 100%, and the method is feasible for the quality level assessment of surrounding rocks, providing a new technique and approach to assessing the risk level of surrounding rocks.

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“…The VCM is an objective weighting method based on statistical methods for calculating the change degree of metrics [28]. It can objectively reflect the change information of factors.…”

Section: Variation Coefficient Methods (Vcm)mentioning

confidence: 99%

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

Li,

Lin,

Qiang

et al. 2024

PLoS ONE

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Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper’s method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment.

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Optimization of water quality index models using machine learning approaches

Cited by 54 publications

References 39 publications

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

The application of the game theory combination weighting-normal cloud model to the quality evaluation of surrounding rocks

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

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