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Author: search.filters.author.Raksuntorn, Nareenart

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    Prediction of Water Quality Index (WQI) Using Machine Learning
    (2025-01-01)
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Boonseng, Chongrag
    The purpose of this project is to assess Water Quality Index (WQI) by using five machine learning techniques including the Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). In this case, we are using Thailand as a base country for assessing water quality of the rivers and canals. The data set was collected from Bangkok Metropolitan Authority of Thailand during the period January 2018 to January 2021. The data set included 43,776 records and each record comprised 12 quantitative measurements related to water quality. Hence, they were used as feature inputs of the assessment model; for instance, pH, DO (Dissolved Oxygen), BOD (Biochemical Oxygen Demand), TP (Total Phosphorus), TCB (Total Coloniform Bacteria), FCB (Fecal Coloniform Bacteria), NO3-N (Nitrogen-Nitrogen), No2-N (Nitrogen-Suspended Solid), NH3-N (Ammonia-Nitrogen), TS (Total Solid), and Total Dissolved Solid (TDS). During the phase of preprocessing K-Nearest Neighbors (KNN) and Random Forest were employed to handle missing data and detecting outliers. KNN imputation was applied to address missing values, while Random Forest was implemented to eliminate outliers, so generating the dataset appropriate for model training. The effectiveness of each machine learning model was assessed employing four principal metrics: accuracy, precision, recall, and F1 score. The findings revealed that all five methodologies excelled in predicting WQI; however, the XGBoost model surpassed the others, attaining the highest values across all metrics, including an accuracy of 91%.
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    Enhancing Student’s Learning by Integrating the Concept of Project-Based and Challenge-Based Learning
    (2025-01-01)
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Roonrakwit, Pattarapan
    ;
    Boonseng, Chongrag
    This study aims to enhance student learning in the introductory data science course by combining Project-Based and Challenge-Based Learning methodologies. The goal is to improve student performance, engagement, and comprehend of data science subjects through research and development. By integrating challenge-based learning (CBL) and project-based learning (PBL) into a cohesive teaching approach, the benefits of both approaches are employed to improve the learning experience for students. This methodology facilitates in-depth comprehension, critical reasoning, and practical application of data science skills, preparing students for academic success and real-world obstacles. The research sample comprised 56 third-year undergraduates from the Computer Science department, selected using a purposive sampling method. Data analysis involves the utilization of several statistical techniques such as the t-test, analysis of covariance (ANCOVA), mean, standard deviation, and other relevant statistics. The findings indicated that students who utilized a blend of Project-Based and Challenge-Based Learning methods had a notable increase in academic performance post-learning, as compared to their pre-learning performance, with statistical significance. Firstly, students at the 05 level are more likely to express their satisfaction with the course of study. This is primarily because the course incorporates problem-based learning (PBL) and case-based learning (CBL), which are both engaging and practical teaching methods. Furthermore, this implies that improving student achievement in the data science course is an effective use of the integrated teaching methodology.
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    Prediction of CO2 emissions using machine learning
    (2024-01-01)
    Bussaban, Kanyarat
    ;
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Boonseng, Chongrag
    Carbon dioxide (CO<inf>2</inf>) contributes significantly to climate change as a greenhouse gas. The Earth's atmosphere is naturally kept warm enough to support life by greenhouse gases which trap heat in the atmosphere. However, human activity has significantly increased the amount of CO2 in the atmosphere because of deforestation and the use of fossil fuels. One of the key concerns with human evolution that fuels global climate change is carbon dioxide (CO<inf>2</inf>). It is released as fuels burn and as a result, people worldwide are gradually becoming more conscious of environmental issues. Effective policy formulation requires an investigation of the factors influencing CO<inf>2</inf> emissions, yet tiny datasets and traditional research methodologies have hampered prior investigations. This research uses three prediction models to estimate CO<inf>2</inf> trapping efficiency among CO<inf>2</inf> emissions, energy use and GDP: Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF). The machine learning (ML) techniques used in this work have demonstrated strong performance with multiple linear regressions, support vector machines and random forest models with mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The investigation has proposed a technique for approximating CO<inf>2</inf> emissions and the results indicate that Support Vector Machine (SVM) can attain the highest degree of precision. The outcome could be a useful model for the decision support system to enhance an appropriate course of action for reducing CO<inf>2</inf> emissions worldwide.
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    Water Quality Index (WQI) Prediction Using Machine Learning Algorithms
    (2023-01-01)
    Kularbphettong, Kunyanuth
    ;
    Waraporn, Phanu
    ;
    Raksuntorn, Nareenart
    ;
    Vivhivanives, Rujijan
    ;
    Sangsuwon, Chanyapat
    Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.