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Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features

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dc.contributor.authorWen Yee Wongen_US
dc.contributor.authorAyman Khallel Ibrahim Al-Anien_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.contributor.authorAnis Salwa Mohd Khairuddinen_US
dc.contributor.authorSarah Abdul Razaken_US
dc.contributor.authorHanee Farzana Hizaddinen_US
dc.contributor.authorMohd Istajib Mokhtaren_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.date.accessioned2024-05-28T06:11:23Z
dc.date.available2024-05-28T06:11:23Z
dc.date.issued2022
dc.date.submitted2022-11-29
dc.descriptionCMES-Computer Modeling in Engineering & Sciences, 132(3), 1011–1038en_US
dc.description.abstractWater quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal–polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed. The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river. The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features. This work analyzes 17 new input features, namely conductivity (COND), salinity (SAL), turbidity (TUR), dissolved solids (DS), nitrate (NO3), chloride (Cl), phosphate (PO4), arsenic (As), chromium (Cr), zinc (Zn), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), E. coli, and total coliform, in predicting WQI using machine learning techniques. Five regression algorithms—random forest (RF), AdaBoost, support vector regression (SVR), decision tree regression (DTR), and multilayer perception (MLP)—are applied for preliminary model selection. The results show that the RF algorithm exhibits better prediction performance, with R2 of 0.974. Then, this work proposes a modified RF by incorporating the synthetic minority oversampling technique (SMOTE) into the conventional RF method. The proposed modified RF method is shown to achieve 77.68%, 74%, 69%, and 71% accuracy, precision, recall, and F1-score, respectively. In addition, the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction. The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.en_US
dc.identifier.citationWong, W. Y., Khallel, A., Hasikin, K., Salwa, A., Razak, S. A. et al. (2022). Water Quality Index Using Modified Random Forest Technique: Assessing Novel Input Features. CMES-Computer Modeling in Engineering & Sciences, 132(3), 1011–1038.en_US
dc.identifier.doi10.32604/cmes.2022.019244
dc.identifier.epage1038
dc.identifier.issn1526-1492
dc.identifier.issue3
dc.identifier.other2494-23
dc.identifier.spage1011
dc.identifier.urihttps://www.techscience.com/CMES/v132n3/48675
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/7147
dc.identifier.volume132
dc.language.isoenen_US
dc.publisherTECH SCIENCE PRESSen_US
dc.relation.ispartofCMES-Computer Modeling in Engineering & Sciencesen_US
dc.subjectArtificial intelligence; random forest; environmental modeling; alternative inputs; SMOTEen_US
dc.titleWater Quality Index Using Modified Random Forest Technique: Assessing Novel Input Featuresen_US
dc.typeArticleen_US
dspace.entity.typePublication

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