[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124311-en":3,"doc-seo-124311-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124311,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms for Rainfall Prediction in Kuantan, Pahang, Malaysia","The study compares four machine learning models—Support Vector Regressor, Artificial Neural Network, Random Forest Regressor, and Linear Regression—for rainfall prediction in Kuantan, Pahang, Malaysia. Temperature, dew point, humidity, wind speed, and pressure are used as input meteorological features, while rainfall amount (mm) is the target output. Using the same parameters across methods, the analysis evaluates predictive quality via Mean Absolute Error and Mean Squared Error, showing Support Vector Regressor achieves the most accurate results with the lowest errors.","Comparative Analysis of Machine Learning Algorithms for Rainfall Prediction in Kuantan, Pahang,  \nMalaysia.  \nSeri Liyana Ezamzuri, Sarah ‘Atifah Saruchi  \nUniversiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia  \nAmmarA.M. Al-Talib  \nUCSI University, 56000 Kuala Lumpur, Malaysia  \n[Email: miz24007@adab.umpsa.edu.my](Email: miz24007@adab.umpsa.edu.my), [sarahatifah@umpsa.edu.my](sarahatifah@umpsa.edu.my), [ammart@ucsiuniversity.edu.my](ammart@ucsiuniversity.edu.my)  \nAbstract  \nThis study compares the performance and accuracy of four ML algorithms which are Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), and Linear Regression (LR) in the rainfall prediction application. All four methods employ the same input parameters which are temperature (°c), dew point (°c), humidity (%), wind speed (Kph) and pressure (Hg) . Meanwhile the output parameter is set to be the rainfall (mm) which indicates the precipitation in Kuantan, Pahang, Malaysia. The analysis shows that the SVR consistently outperforms the other machine learning algorithms, achieving the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE) .  \nKeywords: Machine Learning (ML), Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Linear Regressor (LR), Rainfall prediction.  \n1. Introduction  \nHeavy rainfall affects open water recreational activities due to safety and precaution for natural disasters. Floods, and water surges also known as headwater incidents commonly happen in Malaysia cause from heavy pour which led to increase volume of water and velocity of water current.  \nRecently, there was one case reported due to a water surge incident. Three Jabatan Kerja Raya (JKR) officers were lost in a water surge incident while participating in water rafting activities in Sungai Jahang, Gopeng, Perak [1] . This evidently shows heavy downfall led to water surge, and endangered human lives and natural ecosystem.  \nOn top of that, using advanced technology that we have nowadays, which is Artificial Intelligence (Ai), specifically Machine Learning (ML) to find out the best algorithms for Rainfall Prediction possible outcomes by leveraging patterns in historical data. Hence, this study is to find the best ML algorithms with finest predictions accuracy value.  \nDue to the proximity to the ocean, abundance of rivers, and location on the Malaysian peninsula, Kuantan was selected as the study's location. The dataset used in this study was sourced from Weather and Climate which provides comprehensive historical weather data for various global regions, including Kuantan, Pahang, Malaysia. The dataset includes key meteorological features such as temperature, humidity, wind speed,  \npressure, and precipitation, covering the period from 2018 to 2020.  \n2. Methodology  \nVarious data and models are needed to make a prediction. Generally, we use classification and regression algorithms for time series algorithms [2] . Four ML algorithms have been developed in this study to find the lowest value of MSE and MAE to ensure that algorithm is the most compatible compared others. Other than that, we find the lowest accuracy value in every ML algorithm to support the conclusion in this study.  \nThe methodology for rainfall (precipitation) prediction in Kuantan, Pahang, Malaysia studies by four machine learning algorithms-Artificial Neural Networks (ANN) [3], Support Vector Regression (SVR) [4], Random Forest Regressor (RFR) [5], and Linear Regression [6] . Details of the data collection, data preparation, feature engineering, model training, evaluation process, and calculate correlation coefficient are explained below.  \n2.1 Data Collection  \nA detailed study by [7] analyse various parameters of rainfall prediction and figure out each parameter in meteorological features. Historical weather data for rainfall prediction was obtained from Weather and Climate. Fig.1 shows raw data for","cbCainTiMN5zibjg","https://ap.wps.com/l/cbCainTiMN5zibjg","pdf",692784,1,5,"English","en",105,"# Introduction\n## Problem background and motivation\n## Study location and dataset overview\n# Methodology\n## Data collection\n## Preprocessing steps and tools\n## Model training and evaluation (overview)","[{\"question\":\"Which machine learning algorithms are compared in the rainfall prediction study?\",\"answer\":\"The study compares Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), and Linear Regression (LR) for rainfall prediction.\"},{\"question\":\"What input features and output target are used for the models?\",\"answer\":\"Inputs include temperature, dew point, humidity, wind speed, and pressure, while the output target is rainfall amount measured in millimeters (mm).\"},{\"question\":\"What evaluation results indicate the best-performing algorithm?\",\"answer\":\"Support Vector Regressor consistently yields the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE), indicating the most accurate rainfall predictions among the compared models.\"}]","Comparative Analysis of Machine Learning Algorithms for Rainfall Prediction in Kuantan, Pahang, Malaysia | PDF",1785821518,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparative-analysis-of-machine-learning-algorithms-for-rainfall-prediction-in-kuantan-pahang-malaysia","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-algorithms-for-rainfall-prediction-in-kuantan-pahang-malaysia/124311/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared in the rainfall prediction study?","Question",{"text":75,"@type":76},"The study compares Support Vector Regressor (SVR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), and Linear Regression (LR) for rainfall prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What input features and output target are used for the models?",{"text":80,"@type":76},"Inputs include temperature, dew point, humidity, wind speed, and pressure, while the output target is rainfall amount measured in millimeters (mm).",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation results indicate the best-performing algorithm?",{"text":84,"@type":76},"Support Vector Regressor consistently yields the lowest Mean Absolute Error (MAE) and Mean Squared Error (MSE), indicating the most accurate rainfall predictions among the compared models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]