Assessing optimization techniques for improving water quality model

Uddin, Md Galal; Rahman, Azizur; Olbert, Agnieszka Indiana

doi:10.1016/j.jclepro.2022.135671

Cited by 67 publications

(36 citation statements)

References 58 publications

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“…The details of the findings of this critical review can be found in Uddin et al (2021). Recently, several studies have revealed that the entire indicator selection technique contributed a significant amount of uncertainty to the final assessment due to the inappropriate indicator selection (Gupta and Gupta, 2021;Parween et al, 2022;Sutadian et al, 2016;Uddin et al, 2022aUddin et al, , 2022d. In order to reduce the model uncertainty through the indicator selection process, a few studies have utilized different machine learning algorithms like random forest, support vector machine, tree algorithm, gradient boosting algorithm, k-nearest neighbour, artificial neural network, etc.…”

Section: Indicators Selection Techniquementioning

confidence: 99%

“…Details of the findings of this research can be found in Uddin et al (2022a). Many studies have also recommended the gradient boosting algorithm for selecting important indicators for other waterbodies like lakes, rivers, etc., in a given dataset (Huan et al, 2020;Islam Khan et al, 2022;Naghibi et al, 2020;Touzani et al, 2018;Uddin et al, 2022d. In terms of the reliability of this technique, a few studies reveal that the gradient boosting algorithm does not reflect the actual scenarios of significant indicators that express the relative value of the indicators in terms of influencing coastal water quality (Uddin et al, 2022a(Uddin et al, , 2022d.…”

Section: Indicators Selection Techniquementioning

confidence: 99%

“…Many studies have also recommended the gradient boosting algorithm for selecting important indicators for other waterbodies like lakes, rivers, etc., in a given dataset (Huan et al, 2020;Islam Khan et al, 2022;Naghibi et al, 2020;Touzani et al, 2018;Uddin et al, 2022d. In terms of the reliability of this technique, a few studies reveal that the gradient boosting algorithm does not reflect the actual scenarios of significant indicators that express the relative value of the indicators in terms of influencing coastal water quality (Uddin et al, 2022a(Uddin et al, , 2022d. Moreover, a few studies have reported that the gradient boosting algorithm shows an over fitting problem in predicting important features (Ahmed et al, 2019;Islam Khan et al, 2021;Uddin et al, 2022bUddin et al, , 2022d.…”

Section: Indicators Selection Techniquementioning

confidence: 99%

“…In terms of the reliability of this technique, a few studies reveal that the gradient boosting algorithm does not reflect the actual scenarios of significant indicators that express the relative value of the indicators in terms of influencing coastal water quality (Uddin et al, 2022a(Uddin et al, , 2022d. Moreover, a few studies have reported that the gradient boosting algorithm shows an over fitting problem in predicting important features (Ahmed et al, 2019;Islam Khan et al, 2021;Uddin et al, 2022bUddin et al, , 2022d. Consequently, recently, the authors have carried out a comprehensive assessment of eighteen feature section algorithms to select important indicators for assessing coastal water quality and have analysed the reliability of these techniques (Uddin et al, 2022d).…”

Section: Indicators Selection Techniquementioning

confidence: 99%

“…Recently, several studies have revealed that the entire models have contributed a significant amount of uncertainty to the final assessment of water quality. A number of studies have reported that each component of the model produces its own uncertainty (Uddin et al, 2021(Uddin et al, , 2022c(Uddin et al, , 2022d.…”

Section: Introductionmentioning

confidence: 99%

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Section: Indicators Selection Techniquementioning

confidence: 99%

Section: Indicators Selection Techniquementioning

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Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

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Assessing optimization techniques for improving water quality model

Cited by 67 publications

References 58 publications

A sophisticated model for rating water quality

A sophisticated model for rating water quality

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

A comprehensive review of water quality indices for lotic and lentic ecosystems

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