[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120277-en":3,"doc-seo-120277-105":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},120277,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Temperature and Humidity Prediction Based on Machine Learning","Global climate change increases the need for accurate weather prediction, especially temperature and humidity, because these variables influence agriculture, energy management, and public safety. The study builds and compares multiple machine learning models, including Linear Regression, Support Vector Machine, Neural Network, and Random Forest, to forecast temperature and humidity from historical meteorological data. Results show that the Neural Network model achieves the best accuracy on the dataset, while Random Forest and SVM also perform strongly. Model quality can be further improved through neural hyperparameter tuning and enhanced feature engineering, offering useful tools for decision-making.","Temperature and Humidity Prediction Based on Machine Learning  \nYanqi Xiong  \nSchool of Software, Jiangxi Normal University, Nanchang city, Jiangxi Province, 330000, China  \nAbstract. The growing impact of global climate change, emphasizing the critical importance of accurately predicting weather conditions, particularly temperature and humidity. These predictions are crucial for key sectors such as agriculture, energy management, and public safety. This paper employs various machine learning models, including Linear Regression(LR), Support Vector Machine(SVM), Neural Network(NN), and Random Forest(RF), to analyze their accuracy in predicting temperature and humidity. The results indicate that the NN model outperforms the others, showing excellent performance in the dataset. In addition to the outstanding performance of the neural NN, the RF and SVM also demonstrated strong performance, particularly in handling specific features within the dataset. the model's performance can be further optimized by adjusting the NN's hyperparameters or introducing more feature engineering, which could lead to even better results in future data analyses. This research highlights the significant potential of machine learning techniques in enhancing meteorological forecasting, providing valuable insights and tools for improving decision-making in industries heavily influenced by weather conditions.  \n1 Introduction  \nAs global climate change intensifies, the accuracy of predicting future weather conditions becomes increasingly important. Weather forecasting relies on the accurate measurement and analysis of meteorological parameters such as temperature and humidity [1] . Changes in temperature and humidity directly affect atmospheric circulation patterns , which in turn determine the development and movement of weather systems . Sudden extreme weather events, such as unexpected high or low temperatures, can reduce crop yields and negatively impact farmers' incomes. High humidity or heavy rain can increase the demand for electricity, placing stress on the power grid [2] . Additionally, humidity can impact public transportation safety; slippery roads may lead to an increase in traffic accidents. Therefore, accurately predicting future temperature and humidity is of great significance to society and the economy [3] .  \nTraditional meteorological forecasting methods primarily rely on physical models and statistical techniques. These methods typically require large amounts of historical data and complex mathematical calculations. Challenges such as sensitivity to initial conditions,  \nCorresponding author: [sweetumz2011@email. phoenix. edu](sweetumz2011@email. phoenix. edu)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nhigh computational demands, and insufficient capacity to handle complex nonlinear relationships often result in less than satisfactory prediction accuracy. with the rapid development of big data and computational power, the application of machine learning techniques in weather forecasting has garnered increasing attention . Machine learning algorithms can analyze vast amounts of historical meteorological data , capturing underlying patterns and trends, thereby enhancing predictions' accuracy and efficiency. Numerous studies have demonstrated the advantages of machine learning in meteorological forecasting. For example, R. Kimura and colleagues employed existing data to predict temperature changes. Similarly, Peter Bauer, Alan Thorpe, and Gilbert Brunet utilized deep learning methods to predict weather, and their results showed significantly improved accuracy compared to traditional methods achieving favorable outcomes . These studies indicate that machine learning methods have promising applications in weather forecasting.  \nThe study a","cbCaiupwMxQkEtSs","https://ap.wps.com/l/cbCaiupwMxQkEtSs","pdf",324291,1,9,"English","en",105,"# Introduction\n## Motivation and significance\n## Limitations of traditional methods\n## Research goal and tasks\n# Data and Methods\n## Dataset source and characteristics\n## Data preprocessing and setup","[{\"question\":\"Why is accurate temperature and humidity prediction important?\",\"answer\":\"Temperature and humidity affect atmospheric circulation and the evolution of weather systems. They also influence agriculture yields, energy demand, and public transportation safety.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study compares Linear Regression, Support Vector Machine, Neural Network, and Random Forest to predict temperature and humidity and evaluate their accuracy.\"},{\"question\":\"What are the main findings about model performance?\",\"answer\":\"The Neural Network model outperforms the others on the dataset. Random Forest and SVM also show strong performance, particularly in handling dataset features.\"}]","Temperature and Humidity Prediction Based on Machine Learning | PDF",1785729204,23,{"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},"temperature-and-humidity-prediction-based-on-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/temperature-and-humidity-prediction-based-on-machine-learning/120277/",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","2026-08-03",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},"Why is accurate temperature and humidity prediction important?","Question",{"text":75,"@type":76},"Temperature and humidity affect atmospheric circulation and the evolution of weather systems. They also influence agriculture yields, energy demand, and public transportation safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"The study compares Linear Regression, Support Vector Machine, Neural Network, and Random Forest to predict temperature and humidity and evaluate their accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main findings about model performance?",{"text":84,"@type":76},"The Neural Network model outperforms the others on the dataset. 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