[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124364-en":3,"doc-seo-124364-105":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":20,"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},124364,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","LEVERAGING MACHINE LEARNING FOR RAINFALL PREDICTION IN NORTH-CENTRAL NIGERIA - Comparative Algorithm Study","Rainfall is increasingly difficult to manage due to shifting weather patterns driven by climate change, affecting precipitation behaviour and downstream planning needs. In north-central Nigeria—widely regarded as the nation’s food basket—understanding relationships between rainfall and key atmospheric variables is essential for accurate forecasting and agricultural decision-making. This comparative study evaluates multiple machine learning algorithms using related input variables to predict rainfall outcomes. Results indicate that Random Forest Regression offers strong flexibility for modelling and controlling rainfall patterns, supporting improved guidance for meteorological operations and stakeholders.","G. B. Balogun, D. T. Kudabo, O. J. Peter, A. G. Akintola  \nVolume 30, Issue (4), 2025  \nLEVERAGING MACHINE LEARNING FOR RAINFALL PREDICTION IN NORTH-CENTRAL NIGERIA: COMPARATIVE ALGORITHM STUDY  \nG. B. Balogun (1)  \nD. T. Kudabo (1)  \nO. J. Peter (2)*  \nA. G. Akintola (1)  \nReceived: 27/01/2025  \nRevised: 09/03/2025  \nAccepted: 10/03/2025  \n© 2025 University of Science and Technology, Aden, Yemen. This article can be distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \n2025 ©  \nـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ  \n1 Department of Computer Science, University of Ilorin, Nigeria  \n2 Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City Ondo State, Nigeria  \n*Corresponding Author’s Email: [peterjames4real@gmail.com](peterjames4real@gmail.com)  \n50  \n[https://doi.org/10.20428/jst.v30i4.2771](https://doi.org/10.20428/jst.v30i4.2771)  \nG. B. Balogun, D. T. Kudabo, O. J. Peter, A. G. Akintola  \nVolume 30, Issue (4), 2025  \nLeveraging Machine Learning for Rainfall Prediction in North-Central  \nNigeria: Comparative Algorithm Study  \nG. B. Balogun Department of Computer Science, University of Ilorin, Nigeria [balogun.gb@unilorin.edu.ng](balogun.gb@unilorin.edu.ng)  \nD. T. Kudabo Department of Computer Science, University of Ilorin, Nigeria  \nO. J. Peter  \nDepartment of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City Ondo State, Nigeria  \nA. G. Akintola Department of Computer Science, University of Ilorin, Nigeria  \nAbstract— Rainfall has been a major worry in recent times because of the weather patterns that are constantly changing. The last ten years, in particular, have seen significant changes in rainfall patterns due to global warming. The principal determinants of this rainfall pattern include precipitation, dew points, wind speed, pressure, temperature, and humidity. For the purposes of agricultural growth and precise rainfall forecasting, it is essential to comprehend the relationship between these variables and rainfall behaviour. This is particularly true for the north central region of Nigeria, which is known as the country’s “food basket.” This work aims to investigate how machine learning techniques might infer precipitation patterns in north-central Nigeria. This study looked at several algorithms and evaluated how well they performed in respect to each variable that was connected to the goal variable. We also examine the performance of several machine learning methods in predicting rainfall. The outcome demonstrates the potential of the Random Forest Regression Algorithm as a flexible method for comprehending and controlling rainfall patterns. Thus, it is advised that the Nigerian Meteorological Agency (NIMET) use the outcome in conjunction with the traditional NWP (Numerical Weather Prediction) method to further improve rainfall prediction. The result will help farmers optimise their planting and harvesting schedules, assist water resource managers in planning for different water usages, allow disaster management authorities to issue timely warnings, support the conservation of natural resources, and ultimately promote economic development through infrastructure planning.  \nKeywords—Computer architecture; Neural networks; Predictive models; Biological system modeling; Neurons; Data models; ConvNet; Deep Learning; LSTM; Precipitation; Rainfall Prediction.  \nI. INTRODUCTION  \nAccurate rainfall prediction is vital for managing water resources, supporting sustainable agriculture, and mitigating socioeconomic impacts in regions dependent on rainfall. This is particularly relevant in North Central Nigeria, which features diverse geography, including the Jos Plateau, the Benue River Valley, and the lower Niger River Basin, with annual rainfall ranging from 1,000 to 1,400 millime","cbCaimWpp7kcSAe8","https://ap.wps.com/l/cbCaimWpp7kcSAe8","pdf",1034433,1,18,"English","en",105,"# Introduction\n## Rainfall prediction needs and regional context\n## Machine learning and remote sensing for rainfall modelling\n## Algorithm comparisons and integration with NWP","[{\"question\":\"Why is accurate rainfall prediction important for north-central Nigeria?\",\"answer\":\"It supports water resource management, sustainable agriculture, and reduces socioeconomic impacts. The region’s complex rainfall patterns and exposure to flooding make forecasting especially critical.\"},{\"question\":\"Which variables are considered in the rainfall prediction framework?\",\"answer\":\"The study highlights precipitation determinants such as dew points, wind speed, pressure, temperature, humidity, and related atmospheric factors used as inputs to predict rainfall behaviour.\"},{\"question\":\"What is the main conclusion about algorithm performance?\",\"answer\":\"The results suggest Random Forest Regression is a flexible and effective approach for modelling and controlling rainfall patterns. The study recommends using it alongside traditional numerical weather prediction methods to improve forecasting.\"}]","LEVERAGING MACHINE LEARNING FOR RAINFALL PREDICTION IN NORTH-CENTRAL NIGERIA - Comparative Algorithm Study | PDF",1785821828,45,{"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},"leveraging-machine-learning-for-rainfall-prediction-in-north-central-nigeria-comparative-algorithm-study","",{"@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/leveraging-machine-learning-for-rainfall-prediction-in-north-central-nigeria-comparative-algorithm-study/124364/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is accurate rainfall prediction important for north-central Nigeria?","Question",{"text":75,"@type":76},"It supports water resource management, sustainable agriculture, and reduces socioeconomic impacts. The region’s complex rainfall patterns and exposure to flooding make forecasting especially critical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which variables are considered in the rainfall prediction framework?",{"text":80,"@type":76},"The study highlights precipitation determinants such as dew points, wind speed, pressure, temperature, humidity, and related atmospheric factors used as inputs to predict rainfall behaviour.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main conclusion about algorithm performance?",{"text":84,"@type":76},"The results suggest Random Forest Regression is a flexible and effective approach for modelling and controlling rainfall patterns. The study recommends using it alongside traditional numerical weather prediction methods to improve forecasting.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]