Abstract
Water is an essential resource in Malaysia, playing a crucial role in sustaining human life, agriculture, and industry. However, rapid industrialization, urbanization, and development have significantly deteriorated river water quality, posing serious environmental and public health risks. Traditional water quality monitoring methods rely on manual sampling and laboratory analysis and are often time consuming, labor-intensive, and inefficient. This study aims to overcome these challenges by developing regression-based predictive models to estimate the Water Quality Index (WQI) based on Dissolved Oxygen (DO) measurements. The research utilizes a dataset of 219 river water samples collected between June and November 2023 from the Kaggle database. Statistical validation techniques were applied to assess data distribution and accuracy, including normality tests and error bar plots. Multiple regression techniques were implemented using MATLAB and Python to determine the most effective model. MATLAB’s Linear Regression model demonstrated superior performance among the tested approaches, achieving an R² value of 0.95397 and a Root Mean Square Error (RMSE) of 7.2728. These results highlight the potential of regression models in providing a fast, reliable, and cost-effective method for water quality assessment. By leveraging these predictive techniques, environmental authorities and policymakers can implement timely interventions, ensuring better management and protection of freshwater ecosystems in Malaysia.
Metadata
Item Type: | Article |
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Creators: | Creators Email / ID Num. Wan Roselan, Wan Mohamad Haziq UNSPECIFIED Ahmad Suhkri, Muhamad Irfan UNSPECIFIED Abd Rahman, Mohamad Faizal UNSPECIFIED Sumagayan, Moheddin Usodan UNSPECIFIED Sulaiman, Mohd Suhaimi shemi@uitm.edu.my |
Contributors: | Contribution Name Email / ID Num. Chief Editor Damanhuri, Nor Salwa UNSPECIFIED |
Subjects: | T Technology > TC Hydraulic engineering. Ocean engineering > River, lake, and water-supply engineering |
Divisions: | Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus |
Journal or Publication Title: | ESTEEM Academic Journal |
UiTM Journal Collections: | Listed > ESTEEM Academic Journal (EAJ) |
ISSN: | 2289-4934 |
Volume: | 21 |
Page Range: | pp. 21-30 |
Keywords: | Water Quality Index (WQI), Dissolved Oxygen (DO), Regression Modelling, Machine Learning, Water Quality Assessment, River Pollution |
Date: | March 2025 |
URI: | https://ir.uitm.edu.my/id/eprint/112502 |