[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117435-en":3,"doc-seo-117435-105":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},117435,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","CLIMATE INFORMATICS: CLIMATE PREDICTION AND MACHINE LEARNING","Climate informatics addresses the challenge of forecasting climate change by integrating climate science with data science methods. The approach uses data analysis, modern visualization tools, and advanced computational skills to handle complex outputs generated by climate projects. Evidence links climate deterioration to industrialization, while machine learning leverages datasets to learn patterns and produce predictive climate insights. Data from multiple sources may be influenced positively or negatively through computer science and machine learning, supporting varied analysis types.","The World’s Largest Open Access Agricultural & Applied Economics Digital Library  \nThis document is discoverable and free to researchers across theglobe due to the work of AgEcon Search .  \nHelp ensure our sustainability.  \nGive to AgEcon Search  \nAgEcon Search  \nhttp://ageconsearch.umn.eduaesearch@umn.edu  \nPapers downloaded from AgEcon Search may be used for non-commercial purposes and personal study only.No other use, including posting to another Internet site, is permitted without permission from the copyrightowner (not AgEcon Search), or as allowed under the provisions of Fair Use, U.S. Copyright Act, Title 17 U.S.C.  \nNo endorsement of AgEcon Search or its fundraising activities by the author(s) of the following work or theiremployer(s) is intended or implied.  \nInternational Journal of Agriculture and Environmental Research  \nISSN: 2455-6939  \nVolume: 11, Issue: 02 \"March-April 2025\"  \n# CLIMATE INFORMATICS: CLIMATE PREDICTIONAND MACHINE LEARNING\n\nSurabhi Singh  \nFaculty, Pillai College of Engineering, New Mumbai, India.  \nDOI: https://doi.org/10.51193/IJAER.2025.11208  \nReceived: 11 Mar. 2025 / Accepted: 20 Mar. 2025 / Published: 25 Mar. 2025  \n# ABSTRACT\n\nOne of the important problems in today’s world is climate change, and how can we predict thiseffectively? Climate informatics is a growing technique/field that combines climate science anddata science to understand, model, and predict climate change more effectively. It is evolvingbecause of the combination of data analysis, modern visualization tools, and technically advancedcomputational skills to manage the complicated and problematic data generated by various climateprojects. It has been proved that the deterioration of climate started with the advent ofindustrialization. Since Machine learning has developed in a bigger way in the last few years, thedatasets can be used to be predictive for climate information. In machine learning, machines aretrained with data to perform specific tasks and deliver accurate results. Fundamentally, ArtificialIntelligence is enabling machine learning to analyze with the help of data that is fed into it. Wemay have noticed the fluctuations in the climate because of global warming, ozone depletion, andvarious other reasons, the data obtained from the various sources can be impacted positively ornegatively by computer science and machine learning. There are multiple datasets available in themarket from different sources, here isthe introduction of various types of analysis to interpret thedata sets.  \nKeywords: Climate, machine learning, datasets, atmosphere, environmentINTRODUCTION  \nClimate informatics is a branch that includes research that combines climate science with theaddition of statistics, machine learning, and a set of data.  \nThe growing impact of climate change will have a profound effect on the world’s socialarrangement in the current century and future generations. We can observe the changes intemperature, polar ice, and sea level, which implies that there is a clear need for a better  \nwww.ijaer.in Copyright © 2025 by the authors. Licensed under CC BY-NC-SA 4.0 Page 416  \nInternational Journal of Agriculture and Environmental Research  \nISSN: 2455-6939  \nVolume: 11, Issue: 02 \"March-April 2025\"  \nunderstanding of the climate system. The whole climate data system is a very complicated andcomplex phenomenon. So far, this system has been wrongly observed and even more wronglysimulated. In this era, there is an ever-growing supply of climate data from satellites andenvironmental sensors, and the significance of climate data output cannot be interpreted withrelatively simple tools. Therefore, a computational approach will be crucial for these analysischallenges. This paper explains how computational approaches are being used to analyze data.  \nCLIMATE AND INDUSTRIALIZATION  \nThe effect of Global warming has become more pronounced since the 80s-90s. In the developingcountries, people are dependent on the natural resources fo","cbCaipb0Q7BVP113","https://ap.wps.com/l/cbCaipb0Q7BVP113","pdf",975991,1,8,"English","en",105,"# Abstract\n# Introduction\n# Climate and Industrialization\n# Machine Learning and Data Analysis","[{\"question\":\"What is climate informatics and what problem does it target?\",\"answer\":\"Climate informatics combines climate science with data science to understand, model, and predict climate change more effectively. It targets improving how climate change can be predicted.\"},{\"question\":\"How does machine learning contribute to climate prediction in the paper?\",\"answer\":\"Machine learning trains machines with climate datasets to perform specific tasks and deliver accurate results. The paper explains that datasets can be used predictively for climate information.\"},{\"question\":\"Why is computational analysis considered crucial for climate data?\",\"answer\":\"The climate data system is described as complex, and relatively simple tools cannot interpret the significance of climate data output. A computational approach is presented as necessary for these analysis challenges.\"}]","CLIMATE INFORMATICS: CLIMATE PREDICTION AND MACHINE LEARNING | PDF",1785675866,20,{"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},"climate-informatics-climate-prediction-and-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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/climate-informatics-climate-prediction-and-machine-learning/117435/",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 is climate informatics and what problem does it target?","Question",{"text":75,"@type":76},"Climate informatics combines climate science with data science to understand, model, and predict climate change more effectively. It targets improving how climate change can be predicted.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning contribute to climate prediction in the paper?",{"text":80,"@type":76},"Machine learning trains machines with climate datasets to perform specific tasks and deliver accurate results. The paper explains that datasets can be used predictively for climate information.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is computational analysis considered crucial for climate data?",{"text":84,"@type":76},"The climate data system is described as complex, and relatively simple tools cannot interpret the significance of climate data output. 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