Climate Change Analysis in Malaysia Using Machine Learning
Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges,...
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| Format: | Article |
| Language: | English |
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MMU Press
2025-02-01
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| Series: | Journal of Informatics and Web Engineering |
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| Online Access: | https://journals.mmupress.com/index.php/jiwe/article/view/1361 |
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| author | Anishalache Subramanian Naveen Palanichamy Kok-Why Ng Sandhya Aneja |
| author_facet | Anishalache Subramanian Naveen Palanichamy Kok-Why Ng Sandhya Aneja |
| author_sort | Anishalache Subramanian |
| collection | DOAJ |
| description | Climate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that poses major implications for agriculture, infrastructure resilience, and water management. Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptation methods and offering practical ideas. |
| format | Article |
| id | doaj-art-0fa33d5c4b4f41bcafeb03830e25ea63 |
| institution | DOAJ |
| issn | 2821-370X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MMU Press |
| record_format | Article |
| series | Journal of Informatics and Web Engineering |
| spelling | doaj-art-0fa33d5c4b4f41bcafeb03830e25ea632025-08-20T03:00:25ZengMMU PressJournal of Informatics and Web Engineering2821-370X2025-02-014130731910.33093/jiwe.2025.4.1.221361Climate Change Analysis in Malaysia Using Machine LearningAnishalache Subramanian0Naveen Palanichamy1https://orcid.org/0000-0003-4601-9770Kok-Why Ng2Sandhya Aneja3Multimedia University, MalaysiaMultimedia University, MalaysiaMultimedia University, MalaysiaMarist College, United StatesClimate change presents significant challenges to ecosystems, economies, and societies globally. In Malaysia, a tropical country highly dependent on its natural resources, the impacts are evident in altered rainfall patterns, rising temperatures, and extreme weather events. Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. Performance measures like Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess three ML models: Support Vector Regression (SVR), Random Forest Regression (RFR) and Linear Regression (LR). The findings demonstrate that LR performs better than the other models in forecasting patterns of precipitation and temperature. The results suggest a significant increase in temperature and unpredictable patterns of precipitation, and that poses major implications for agriculture, infrastructure resilience, and water management. Malaysia's climate resilience is improved by this research, which promotes data-driven policymaking by assessing current climate adaptation methods and offering practical ideas.https://journals.mmupress.com/index.php/jiwe/article/view/1361machine learningtemperatureprecipitationsupport vector regression random forestlinear regression |
| spellingShingle | Anishalache Subramanian Naveen Palanichamy Kok-Why Ng Sandhya Aneja Climate Change Analysis in Malaysia Using Machine Learning Journal of Informatics and Web Engineering machine learning temperature precipitation support vector regression random forest linear regression |
| title | Climate Change Analysis in Malaysia Using Machine Learning |
| title_full | Climate Change Analysis in Malaysia Using Machine Learning |
| title_fullStr | Climate Change Analysis in Malaysia Using Machine Learning |
| title_full_unstemmed | Climate Change Analysis in Malaysia Using Machine Learning |
| title_short | Climate Change Analysis in Malaysia Using Machine Learning |
| title_sort | climate change analysis in malaysia using machine learning |
| topic | machine learning temperature precipitation support vector regression random forest linear regression |
| url | https://journals.mmupress.com/index.php/jiwe/article/view/1361 |
| work_keys_str_mv | AT anishalachesubramanian climatechangeanalysisinmalaysiausingmachinelearning AT naveenpalanichamy climatechangeanalysisinmalaysiausingmachinelearning AT kokwhyng climatechangeanalysisinmalaysiausingmachinelearning AT sandhyaaneja climatechangeanalysisinmalaysiausingmachinelearning |