Frontiers in Environmental Science - 2023 : Enhancement Of Water Quality Index Prediction Using Support Vector Machine With Sensitivity Analysis

Dr. Fatimah Bibi, Hamzah (2023) Frontiers in Environmental Science - 2023 : Enhancement Of Water Quality Index Prediction Using Support Vector Machine With Sensitivity Analysis. Frontiers in Environmental Science, 10. pp. 1-21. ISSN 2296665x

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Abstract

For more than 25years, the Department of Environment (DOE) of Malaysia has implemented a water quality index (WQI) that uses six key water quality parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, ammoniacal nitrogen (AN), and suspended solids (SS). Water quality analysis is an essential component of water resources management that must be properly managed to prevent ecological damage from pollution and to ensure compliance with environmental regulations. This increases the need to define an efficient method for WQI analysis. One of the major challenges with the current calculation of the WQI is that it requires a series of sub-index calculations that are time consuming, complex, and prone to error. In addition, the WQI cannotbecalculatedifoneormorewaterqualityparametersaremissing.Inthis study, the optimization method of WQI was developed to address the complexity of the current process. The potential of data-driven modeling, i.e., Support Vector Machine (SVM) based on Nu-Radial basis function with 10-fold cross-validation, was developed and explored to improve the prediction of WQI in Langat watershed. A thorough sensitivity analysis under six scenarios was also conducted to determine the efficiency of the model in WQI prediction. In the first scenario, the model SVM-WQI showed exceptional ability to replicate the DOE-WQI and obtained statistical results at a very high level (correlation coefficient, r > 0.95, Nash Sutcliffe efficiency, NSE >0.88, Willmott’s index of agreement, WI >0.96). Inthesecond scenario, themodeling process showed that the WQI can be estimated without any of the six parameters. It can be seen that the parameter DO is the most important factor in determining the WQI. The pH is the factor that affects the WQI the least. Moreover, scenarios three to six show the efficiency of the model interms of timeandcostbyminimizingthenumberofvariablesintheinputcombination of the model (r > 0.6, NSE >0.5 (good), WI > 0.7 (very good)). In summary, the modelwill greatly improve andaccelerate data-driven decision making inwater quality management by making data more accessible and attractive without human intervention.

Item Type:	Article
Uncontrolled Keywords:	data-driven, cross-validation, model prediction, sensitivity analysis, support vector machine, water quality inde
Divisions:	Institute of Graduate Studies (IGS)
Depositing User:	LIBRARY2 UPTM
Date Deposited:	23 Jun 2025 08:42
Last Modified:	23 Jun 2025 08:42
URI:	http://eprints.kuptm.edu.my/id/eprint/4605

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