Performance analysis of the water quality index model for predicting water state using machine learning techniques

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

doi:10.1016/j.psep.2022.11.073

Cited by 114 publications

(36 citation statements)

References 95 publications

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“…The water quality index (WQI) model is a widely used tool for evaluating water quality (WQ). This simple yet powerful tool facilitates the transformation of large quantities of often-complex intercorrelated WQ data into a single numerical value (Uddin et al, 2017;2022a;2023). Its popularity has grown steadily in recent years as a result of its simple architecture, ease of application and straightforward result interpretation.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

Self Cite

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Section: Introductionmentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

Self Cite

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“… Reference Year ML techniques Purpose Jamshid Zadeh et al [ 165 ] 2024 BiLSTM, SVM EC, TDS Rahu, Mushtaque Ahmed et al [ 77 ] 2024 SVM, RF, linear regression, Naive Bayes, and DT Forecasting Agricultural Water Needs Uddin et al [ 166 ] 2023 GPR Predicting WQI Hu et al [ 167 ] 2023 Least Absolute Shrinkage and Selection Operator (Lasso), PCR, Resilient Backpropagation (RPROP), Generalized Regression Neural Network (GRNN), Bidirectional Recurrent Neural Network (BRNN), RF, SVR, GPR,MLR Forecasting the formation of disinfection byproducts (DBPs). Omeka [ 168 ] 2023 MLP-ANNs, MLR Predicting WQI Uddin et al [ 169 ] 2023 SVM, Naïve Bayes (NB), RF, k-NN, XG- Boost Predicting WQI Lap et al [ 170 ] 2023 LR, MLP, SVM, DT, RF Predicting WQI Yan et al [ 171 ] 2023 SVM, RF, Adaboost, and gradient boosting decision tree (GBDT), the Bayesian algorithm Predicting WQ levels Narita et al [ 172 ] 2023 RF, XGBoost, and LightGBM, Forecasting pesticide detectability in surface water Chen et al [ 173 ] 2023 RF, GBRT, XGBoost, ...…”

Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

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“…Various water quality indices have been developed since 1965, with each customised based on the relevant water resource in a specific region (Horton, 1965 ). Most of these indices tend to differ based on the water quality parameters used to develop them, the calculation algorithm and the scale used to rate the water quality (Tyagi et al, 2013 ; Feng et al, 2016 , Malek et al, 2022 , Uddin et al, 2023c ). These indices simplify complex water quality data for political decision-makers, water resource managers who are not technically inclined and the public (Mladenovic-Ranisavljevic & Žerajic, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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

Mogane

Masebe

Msagati

et al. 2023

Environ Monit Assess

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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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Performance analysis of the water quality index model for predicting water state using machine learning techniques

Cited by 114 publications

References 95 publications

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

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

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