[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122276-en":3,"doc-seo-122276-105":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},122276,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms","Rainfall prediction remains challenging due to erratic rainfall patterns and climate-driven fluctuations. Accurate timing and estimation can help reduce flood risks, protect life and assets, and support agricultural planning and crop growth. The study builds weather-forecasting models using daily meteorological inputs such as pressure, humidity, wind speed, latitude, longitude, and precipitable water, leveraging historical trends to guide supervised learning. Classification techniques including Random Forest, KNN, Decision Tree, and Logistic Regression are trained and evaluated after data normalization, with training/test splits supporting reliable prediction.","A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms  \nS Sindhura1*, P Dedeepya2, N Sampreet Chowdary2, Katragadda Megha Shyam3, Balamurali Krishna Thati 4  \n1 Department of Computer Science and Engineering, NRI Institute of Technology, Agiripalli, India  \n2 Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India.  \n3 Department of CSE, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India  \n4 Department of CSE, Dhanekula Institute of Engineering & Technology, Ganguru, Vijayawada., India  \n*[ssindhurapraveen@gmail.com](ssindhurapraveen@gmail.com)  \nAbstract  \nThe difficult challenge of predicting rainfall is brought on by the daily observations of erratic rainfall patterns and climatic fluctuations. Predicting when the rain will fall can help avoid floodsand even aid in crop growth in agriculture. Timely and precise predictions can prevent loss of life and assets. The ability to forecast the amount of rainfall requires an understanding of weatherrelated elements such as pressure, humidity, wind speed, latitude, longitude, and precipitable water with varying x and y-axis parameters. The research in this study involves using fundamental machine learning techniques to create weather forecasting models that use the day's meteorological data to predict whether or not it will rain tomorrow. By utilizing previously identified trends from historical meteorological data, machine learning helps forecast rainfall. We are using a classification model in our supervised data model, and the techniques utilized to forecast the amount of rain include random forest, KNN, decision tree, and logistic regression. Using machine learning algorithms to examine past weather data and find patterns that can be applied to forecast future rainfall patterns is the suggested technique for rainfall prediction. A more accurate weather forecast is made possible by all of the aforementioned factors. We will handle the information that aids in eliminating erroneous and incomplete data. As a component of data preparation, normalization helps to improve feature approximation by adjusting the range of independent variables. Model training is carried out following data preparation, during which data is divided into training and test sets. The test set aids in prediction-making, while the training set serves as the foundation for model training.  \nKeywords: Machine learning, Rainfall prediction, Supervised learning, Weather forecasting  \nINTRODUCTION  \nThe management of water levels and agriculture may be significantly impacted by the unpredictable nature of rainfall brought on by weather changes. Therefore, to lessen the possible negative effects of rainfall variability, accurate rainfall prediction models must be developed. A promising solution to this problem is to combine machine learning with  \nenvironmental data analysis and the analysis of significant atmospheric features. Researchers have made great strides toward creating more accurate and dependable rainfall prediction models, which are crucial for several applications, such as water resource management, agricultural decision-making, and smart city planning. These models are made possible by utilizing historical weather data and sophisticated machinelearning algorithms.  \nSeveral algorithms can be used in the process of forecasting rainfall when predicting rainfall using machine learning. By uncovering latent patterns in past weather records, machine learning techniques can be utilized to forecast rainfall. Various machine learning models have been utilized for this purpose, including Gaussian naïve Bayes, RF, adaptive boosting, gradient boosting, MLP, linear regression, XGBoost, and further ones. To enhance the precision of rainfall forecasting, these algorithms are assessed and contrasted with real-time meteorological datasets and environmental data. Planning for agriculture and efficient water resour","cbCaikInM8CdCRyP","https://ap.wps.com/l/cbCaikInM8CdCRyP","pdf",720243,1,13,"English","en",105,"# Abstract\n# Introduction\n## Motivation for rainfall prediction\n## Machine learning approaches and datasets\n## Limitations of common algorithms","[{\"question\":\"Why is rainfall prediction difficult and why does it matter?\",\"answer\":\"Rainfall is difficult to predict because of erratic patterns and climate fluctuations. Timely and precise forecasts support flood prevention and agriculture by enabling better planning and decision-making.\"},{\"question\":\"Which machine learning models are used for the rainfall prediction task?\",\"answer\":\"The document uses supervised classification models including Random Forest, KNN, Decision Tree, and Logistic Regression, and discusses other methods like Gaussian Naive Bayes, SVM, and gradient-based models in the context of rainfall forecasting.\"},{\"question\":\"How does the study prepare data and train the models?\",\"answer\":\"Data preparation includes normalization to improve feature approximation. After preparation, the dataset is split into training and test sets, where training supports model learning and the test set supports prediction evaluation.\"}]","A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms | PDF",1785809780,33,{"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},"a-comparative-analysis-of-rainfall-prediction-using-optimized-machine-learning-algorithms","",{"@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/a-comparative-analysis-of-rainfall-prediction-using-optimized-machine-learning-algorithms/122276/",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 rainfall prediction difficult and why does it matter?","Question",{"text":75,"@type":76},"Rainfall is difficult to predict because of erratic patterns and climate fluctuations. Timely and precise forecasts support flood prevention and agriculture by enabling better planning and decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used for the rainfall prediction task?",{"text":80,"@type":76},"The document uses supervised classification models including Random Forest, KNN, Decision Tree, and Logistic Regression, and discusses other methods like Gaussian Naive Bayes, SVM, and gradient-based models in the context of rainfall forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study prepare data and train the models?",{"text":84,"@type":76},"Data preparation includes normalization to improve feature approximation. 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