[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117763-en":3,"doc-seo-117763-105":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},117763,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Short-Term Rainfall Prediction Using Supervised Machine Learning","Floods and rain significantly affect the economy of many agricultural countries, making early forecasting essential for disaster risk reduction. This paper proposes a machine learning and data-driven approach to predict short-term rainfall with high accuracy. Using an Australian weather dataset, multiple supervised classification algorithms are trained and evaluated, with models compared using standard performance metrics. Results indicate that the hist gradient boosting classifier achieves the best accuracy (91%) along with strong F1 and ROC-AUC performance.","Advances in Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nShort-Term Rainfall Prediction Using Supervised Machine Learning  \nNusrat Jahan Prottasha 1,*, Anik Tahabilder2, Md Kowsher3, Md Shanon Mia 1, Khadiza Tul Kobra 1  \n1Department of Computer Science, Daffodil International University, Dhaka, Bangladesh 2Department of Computer Science, Wayne State University, Detroit, Michigan, USA 3Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA  \nReceived 30 August 2021; received in revised form 16 May 2022; accepted 02 June 2022  \nDOI: [https://doi.org/10.46604/aiti.2023.8364](https://doi.org/10.46604/aiti.2023.8364)  \nAbstract  \nFloods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.  \nKeywords: rain prediction, machine learning, supervised classification, agriculture resource, crops yield  \n1. Introduction  \nAgriculture plays a vital role in the development of many developing countries [1] . IoT-based smart agriculture model is being implemented worldwide to increase crop yields. The use of intelligent tools in farming can increase the production of crops and also minimize the damage due to disasters. The economy of South Asian countries, including Bangladesh, India, China, and Pakistan, depends more on agriculture. But there are always some natural disasters, including rain and floods, that create huge demolition of crops and property.  \nTherefore, a good rain prediction model is necessary to forecast the rain to reduce the risk to life and also to maintain the agriculture farms in a better way. In addition, a rain prediction model helps farmers take early flood measurements and properly manage water resources.  \nObserving the significance of rain prediction, researchers have developed a lot of devices to predict rainfall, but none of them is worth noting in terms of short-term rain prediction. Hence, it has not been adopted eventually by the end-level user to forecast the rain situation. However, machine learning techniques can make a more accurate prediction because of their underlying technology. Researchers have implemented neural networks (NN) in rainfall prediction and showed that the NN-based model usually exceeds the performance of the numerical weather prediction model.  \nThis study aims to develop a short-term rain prediction model that can effectively and accurately predict rainfall. In this proposed work, several relevant machine learning models have been used to predict rainfall, and finally, a performance comparison has been made to determine the best suitable model.  \n* Corresponding author. E-mail address: [jahannusratprotta@gmail.com](jahannusratprotta@gmail.com)  \n[Tel.:](Tel.:) +8801778-111461  \n2 Advances in Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nIn this project, the twenty-nine most optimistic classifiers have been used from eleven different categories. All these models have been trained and tested with a relevant rainfall dataset to implement this prediction model. The data was collected from a popular and recognized public repository and split into training, validation, and testing data. Since the raw data came from natural weath","cbCain30r9Z8irsc","https://ap.wps.com/l/cbCain30r9Z8irsc","pdf",741810,1,10,"English","en",105,"# Introduction\n## Related Work\n## Methodology and Technology Components\n## Experiments and Results\n## Conclusion and Future Work","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses the need for early prediction of short-term rainfall to reduce damage from floods and support agricultural planning.\"},{\"question\":\"Which machine learning approach is used for rainfall prediction?\",\"answer\":\"The study uses supervised machine learning classification models trained on a weather dataset, then compares their predictive performance.\"},{\"question\":\"Which model performed best and how was it evaluated?\",\"answer\":\"The hist gradient boosting classifier achieved the highest accuracy (91%). Performance was assessed using standard metrics including F1 score and ROC-AUC.\"}]","Short-Term Rainfall Prediction Using Supervised Machine Learning | PDF",1785679442,25,{"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},"short-term-rainfall-prediction-using-supervised-machine-learning","",{"@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/short-term-rainfall-prediction-using-supervised-machine-learning/117763/",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","2026-08-02",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},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses the need for early prediction of short-term rainfall to reduce damage from floods and support agricultural planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approach is used for rainfall prediction?",{"text":80,"@type":76},"The study uses supervised machine learning classification models trained on a weather dataset, then compares their predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and how was it evaluated?",{"text":84,"@type":76},"The hist gradient boosting classifier achieved the highest accuracy (91%). 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