Proposal for a new customization process for a data-based water quality index using a random forest approach

Lee, Hansaem; Park, Seonyoung; Nguyen, Hang Vo-Minh; Shin, Hyun-Sang

doi:10.1016/j.envpol.2023.121222

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…At present, most studies on surface water are mainly focused on revealing the characteristics of surface water pollution (Lee et al, 2023;Wronski and Brooks, 2023;Yan et al, 2023), analyzing the sources of pollutants (Aftab et ), while the research on surface water pollution areas is relatively lacking and has limitations. Surface water management is similarly constrained by the limitations of current regional studies of surface water pollution, which tend to remain in discrete analyses.…”

Section: Introductionmentioning

confidence: 99%

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Wei,

Qiu,

Liu

et al. 2024

Preprint

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Maintaining good surface water quality is essential to protecting ecosystems and human health, and different targeted measures for different polluted areas are an effective way to maintain good water quality. This paper takes Henan as an example to study the spatial dependence of surface water quality and explore its spatial clustering pattern, and find out the main driving factors affecting the water quality and analyze the sources of heavily polluted areas by random forest. The results indicate that the spatial pollution pattern of surface water quality in Henan Province can be roughly categorized as insignificant pollution in the northern part, heavy pollution in the central part, and light pollution in the southern part. The heavily polluted areas are mainly located in Zhengzhou, Luoyang and Kaifeng cities. The main indicators affecting water quality in heavily polluted areas are NH3-N, CODMn and TP. The main causes of the deterioration in the region are urban sewage and industrial wastewater discharges. The results not only provide a scientific basis for the systematic management of surface water quality pollution in Henan Province, but also provide a new method for regional water pollution management.

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Section: Introductionmentioning

confidence: 99%

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Wei,

Qiu,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, comprehensive evaluations take into account the status of various participating indicators, leading to reliable and comprehensive assessment results that are intuitive and easy to understand. As part of the comprehensive evaluation approach, the WQI has been widely applied and researched [13][14][15][16][17][18]. The WQI model generally includes four components: the selection of water quality indicators, assigning scores to the indicators, determining the weights of the indicators, and aggregating the results.…”

Section: Introductionmentioning

confidence: 99%

“…The aggregation function, as the final step in the Water Quality Index (WQI) model, can to some extent determine the general direction of water quality assessment results [10,16]. Aggregation functions are divided into two main categories: weighted aggregation functions and unweighted aggregation functions [2].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Quality Assessment Based on the Random Forest Water Quality Index—Taking Karamay City as an Example

Xiong,

Zhang,

Sun

et al. 2023

Sustainability

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In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: “Excellent”, “Good”, “Medium”, “Poor”, or “Unacceptable”. Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, CODMn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking.

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Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm

Li,

Zhang,

Chen

et al. 2024

J of Env Quality

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High‐precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm‐random forest (ChOA‐RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: “The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water.” Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3‐N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3‐N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA‐RF model is compared with the traditional empirical value model and the particle swarm optimization‐random forest (PSO‐RF) model. The results show that the ChOA‐RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO‐RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high‐precision evaluation of regional water environment quality but also for other machine learning fields.

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Proposal for a new customization process for a data-based water quality index using a random forest approach

Cited by 7 publications

References 28 publications

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Identification of surface water quality pollution areas and pollution sources based on spatial clustering and random forest in Henan, China

Groundwater Quality Assessment Based on the Random Forest Water Quality Index—Taking Karamay City as an Example

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm

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