[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126475-en":3,"doc-seo-126475-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126475,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Climate Change Analysis in Malaysia Using Machine Learning","Climate change creates systemic pressure on Malaysia’s ecosystems, economy, and public systems through changing rainfall regimes, escalating temperatures, and more frequent extreme events. Traditional statistical approaches often restrict the accuracy of climate forecasting and the design of actionable policy responses. This study uses extensive historical climate data and advanced machine learning to forecast precipitation and surface air temperature variations. Three models—SVR, random forest regression, and linear regression—are evaluated using MAE, MSE, and RMSE.","Journal of Informatics and Web Engineering  \nVol. 4 No. 1 (February 2025) eISSN: 2821-370X  \nClimate Change Analysis in Malaysia Using  \nMachine Learning  \nAnishalache Subramanian1, Naveen Palanichamy2,*, Kok-Why Ng3, Sandhya Aneja4  \n1,2,3Faculty of Computing and Informatics Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Malaysia.  \n4School of Computer Science and Mathematics, Marist College, Poughkeepsie, NY12601, United States.  \n*corresponding author: ([p.naveen@mmu.edu.my](p.naveen@mmu.edu.my); ORCiD: 0000-0003-4601-9770)  \nAbstract-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.  \nKeywords—Machine Learning, Temperature , Precipitation, Support Vector Regression, Random Forest, Linear Regression  \nReceived: 01 October 2024; Accepted: 17 December 2024; Published: 16 February 2025  \nThis is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nClimate change presents huge challenges for Malaysia, a tropical country whose economy is highly dependent on natural resources. Climate change affects the rise in temperature and irregular patterns of precipitation that impact agriculture, public health, and infrastructure. Increases in temperature cause increased incidences of heatwaves, reduced agricultural productivity, and enhanced vulnerability to health risks. Meanwhile, erratic rainfall increases the intensities of urban flooding, which causes infrastructural damages and complicates the works associated with urban planning. The increasing occurrence of such extreme weather phenomena as floods and droughts in Malaysia poses a huge risk to ecosystems and critical services like the management of water and energy resources [1] .  \nML techniques may be an encouraging approach in improving climate forecast accuracy [2], [3], [4], [5] . ML can deal with huge and complex data sets, and it could discover patterns that may be missed by traditional statistical methods.  \nThe benefits of ML in discovering and revealing patterns from data do, however, come with challenges such as overfitting and dependency on good-quality data. Prior research has uncovered a pattern linking rises in temperature with falls in agricultural production levels for Malaysia, which has particularly focused on basic crops like rice. Another issue that arises from changes in rain fall patterns is a greater potential for flooding.  \nThis study builds on such findings by using advanced ML approaches to analyze temperature and precipitation data, which, therefore, bring insights that ","cbCaib4cyeMP50GO","https://ap.wps.com/l/cbCaib4cyeMP50GO","pdf",769736,14,1,13,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n## Machine Learning and Deep Learning Approaches\n## Model Evaluation and Performance Metrics","[{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates three models: Support Vector Regression (SVR), Random Forest Regression (RFR), and Linear Regression (LR).\"},{\"question\":\"How is model performance measured?\",\"answer\":\"Performance is assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).\"},{\"question\":\"What key finding does the study report about temperature and precipitation trends?\",\"answer\":\"The results indicate stronger temperature increases and more unpredictable precipitation patterns, implying significant impacts for agriculture, infrastructure resilience, and water management.\"}]","Climate Change Analysis in Malaysia Using Machine Learning | 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machine learning models are evaluated in the study?","Question",{"text":77,"@type":78},"The study evaluates three models: Support Vector Regression (SVR), Random Forest Regression (RFR), and Linear Regression (LR).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is model performance measured?",{"text":82,"@type":78},"Performance is assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).",{"name":84,"@type":75,"acceptedAnswer":85},"What key finding does the study report about temperature and precipitation trends?",{"text":86,"@type":78},"The results indicate stronger temperature increases and more unpredictable precipitation patterns, implying significant impacts for agriculture, infrastructure resilience, and water 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