[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118486-en":3,"doc-seo-118486-105":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},118486,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","INTEGRATING PREDICTIVE ANALYTICS AND MACHINE LEARNING FOR WEATHER FORECASTING - decision-tree-focused methodological framework","Predictive analytics enables accurate forecasting of future weather and environmental conditions by combining statistical modeling and machine learning with large-scale sensor, satellite, and station data. The study evaluates weather-forecasting performance using scatter plots, Ordinary Least Squares (OLS) outputs, error computations, and accuracy assessments, with a particular focus on decision tree models. The proposed workflow supports reliable model building and enables more precise predictions of variables such as air quality, humidity, precipitation, and temperature. Results indicate substantial improvement in forecasting accuracy through machine learning.","INTEGRATING PREDICTIVE ANALYTICSAND MACHINE LEARNING FOR WEATHER FORECASTING  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nINTEGRATING PREDICTIVE ANALYTICS AND MACHINE LEARNING FOR WEATHER FORECASTING  \nDhirendra Kumar Gupta  \nDepartment of Statistics and Operations Research College of Science, Qassim University  \nQassim-51911, Saudi Arabia [E-mail :d.gupita@qu.edu.sa](E-mail :d.gupita@qu.edu.sa)  \nKEYWORDS  \nPrediction, Weather Forecasting, Accuracy, Climate, Preprocessing  \nABSTRACT  \nTo make accurate predictions about future weather and environmental conditions, predictive analytics makes use of cutting-edge data analysis tools like statistical modeling and machine learning. Predictive models are able to offer precise forecasts of important environmental variables including air quality, humidity, precipitation, and temperature by evaluating massive information collected from sensors, satellites, and weather stations. This study provides a comprehensive examination of findings from weather forecasting utilizing scatter plots, outputs from Ordinary Least Squares (OLS) models, computations of errors, and evaluations of accuracy, with a special emphasis on decision tree models. This methodological framework greatly aids in the progress of machine learning techniques by guaranteeing the creation of trustworthy models that can accurately anticipate future outcomes. The results show that machine learning approaches in weather forecasting have made great strides, leading to more accurate predictions.  \nI. INTRODUCTION  \nPredictive analytics for weather and environmental monitoring is a rapidly evolving discipline that uses complex statistical models, machine learning methods, and large volumes of data to predict weather conditions and monitor changes in the environment. The capacity to accurately anticipate weather patterns and environmental phenomena is more important than ever due to the fact that climate change and environmental degradation continue to provide major problems to human civilizations and natural ecosystems. Scientists and meteorologists may use predictive analytics to anticipate a variety of atmospheric variables, including temperature, precipitation, humidity, wind speed, and pressure. These variables are important for understanding both short-term weather occurrences and long-term climate trends. This method, which is based on data, makes it possible to create models that not only provide more precise weather forecasts but also provide more detailed information on changes in the environment, including air and water quality, deforestation, pollution, and even the migratory patterns of different species.  \nThe first step in the process of predictive analytics is to gather a vast amount of data from a variety of sources. These sources include ground-based weather stations, satellites, weather balloons, remote sensing technologies, and environmental monitoring networks. These data sources provide a complete picture of the atmosphere and the environment by recording realtime information that represents the intricacies of weather systems and biological processes. The following phase is data processing, which entails cleaning, standardizing, and organizing  \nINTEGRATING PREDICTIVE ANALYTICSAND MACHINE LEARNING FOR WEATHER FORECASTING  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nraw data so that it can be analyzed. After that, this data is analyzed using a variety of modeling approaches which are able to find patterns and correlations in the data. After then, these patterns may be utilized to forecast meteorological events that will happen in the future or to evaluate environmental concerns, giving important information about what might happen in certain situations.  \nThe capacity of predictive analytics to offer early warnings is one of the most important benefits of using it in weather and environmental monitoring. These early warnings may help save lives, safeguard proper","cbCaiiCSTtyIIZLl","https://ap.wps.com/l/cbCaiiCSTtyIIZLl","pdf",235029,1,9,"English","en",105,"# I. Introduction\n## Data collection and preprocessing\n## Modeling and prediction use cases\n## Benefits: early warnings and planning\n## Climate change and adaptation","[{\"question\":\"How does predictive analytics improve weather forecasting accuracy?\",\"answer\":\"It uses statistical modeling and machine learning to analyze large volumes of data from sensors, satellites, and stations, producing more precise forecasts of key environmental variables.\"},{\"question\":\"What steps are involved in the predictive analytics workflow described in the document?\",\"answer\":\"The process includes gathering extensive data, cleaning and standardizing it for analysis, then applying multiple modeling approaches to identify patterns and correlations for future prediction.\"},{\"question\":\"Why are decision tree models emphasized in the study?\",\"answer\":\"The study provides a methodological examination of forecasting results with a special emphasis on decision tree models, assessing errors and accuracy to support dependable forecasting.\"}]","INTEGRATING PREDICTIVE ANALYTICS AND MACHINE LEARNING FOR WEATHER FORECASTING - decision-tree-focused methodological framework | PDF",1785683835,23,{"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},"integrating-predictive-analytics-and-machine-learning-for-weather-forecasting-decision-tree-focused-methodological-framework","",{"@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/integrating-predictive-analytics-and-machine-learning-for-weather-forecasting-decision-tree-focused-methodological-framework/118486/",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},"How does predictive analytics improve weather forecasting accuracy?","Question",{"text":75,"@type":76},"It uses statistical modeling and machine learning to analyze large volumes of data from sensors, satellites, and stations, producing more precise forecasts of key environmental variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What steps are involved in the predictive analytics workflow described in the document?",{"text":80,"@type":76},"The process includes gathering extensive data, cleaning and standardizing it for analysis, then applying multiple modeling approaches to identify patterns and correlations for future prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are decision tree models emphasized in the study?",{"text":84,"@type":76},"The study provides a methodological examination of forecasting results with a special emphasis on decision tree models, assessing errors and accuracy to support dependable 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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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":106,"slug":137},19,"General","general"]