[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120928-en":3,"doc-seo-120928-105":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},120928,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning-Based System for Weather Prediction and Air Quality Index Estimation - Study Overview","This paper proposes a machine-learning based system for improving weather forecasting and Air Quality Index (AQI) estimation by combining historical weather records with real-time atmospheric measurements from the OpenWeather API. Random Forest models are used to build predictive pipelines, while backend services are implemented with Django deployed on AWS EC2 and fronted by Nginx as a reverse proxy. A ReactJS web interface is hosted on AWS S3 and served via CloudFront, supported by a dedicated mobile app for real-time weather and AQI updates, enabling more informed decisions and stronger environmental awareness.","Machine Learning-Based System for Weather Prediction and Air  \nQuality Index Estimation  \nAdnan Mohammed 1, S. Roshan Zameer2, Umar Chowdhry3 and Dr. Ashok Kumar4 1Undergraduate Student, Department of Information Science and Engineering, B.M. S College of Engineering, INDIA 2Undergraduate Student, Department of Information Science and Engineering, B.M. S College of Engineering, INDIA 3Undergraduate Student, Department of Information Science and Engineering, B.M. S College of Engineering, INDIA 4Professor, Department of Information Science and Engineering, B.M. S College of Engineering, INDIA  \n1Corresponding Author: [adnan.is20@bmsce.ac.in](adnan.is20@bmsce.ac.in)  \nReceived: 20-03-2024 Revised: 08-04-2024 Accepted: 28-04-2024  \nABSTRACT  \nThis paper presents a study on \"Machine Learning for Weather Prediction and Air Quality Index Estimation,\"aimed at enhancing weather forecasting and air quality monitoring. Integrating historical weather data with realtime atmospheric measurements from the OpenWeather API, the study utilizes the Random Forest Machine Learning algorithm to construct predictive models. Backend operations are managed by a Django application on AWS EC2, supported by Nginx as a reverse proxy. The frontend, a ReactJS-based web app hosted on AWS S3 and distributed via CloudFront, offers an intuitive interface. Additionally, a dedicated mobile app extends the system's reach, delivering real-time updates on weather conditions and air quality. This comprehensive approach empowers users with precise insights for informed decision-making and environmental awareness.  \nKeywords— Air Quality Index, Django, Random Forest, Weather Prediction, CloudFront, ReactJs, Webview  \nI. INTRODUCTION  \nWeather forecasting is vital across sectors, safeguarding lives, property, and economic activities from the adverse impacts of extreme weather events such as storms, floods, and heatwaves [1] . However, accurately predicting weather conditions remains a complex and challenging task, especially considering the rapid shifts in environmental conditions due to climate change [4, 18] . The field of meteorology heavily relies on precise weather forecasts to ensure safety planning, effective industry operations, and informed economic decision-making [6, 9] . For example, agricultural forecasts, which rely on factors like temperature, humidity, wind, and precipitation outlook, are crucial for crop planning, trading in agricultural markets, and ensuring food security [1] . Similarly, utility companies use temperature forecasts to estimate future energy demands, while individuals and businesses rely on weather forecasts to plan activities and  \nmitigate risks associated with adverse weather conditions [1] .  \nAnalyzing vast amounts of meteorological data presents significant challenges, which are addressed through various data mining procedures, including machine learning techniques [1] . Machine learning plays a pivotal role in weather prediction and Air Quality Index (AQI) estimation, establishing critical links between weather patterns and air quality [1, 17] . The correlation between climate, weather, and air quality profoundly impacts human health, with air pollution contributing to millions of premature deaths worldwide [1] . Therefore, weather forecasting and AQI estimation become essential tools in managing and mitigating the adverse health effects of air pollution [1] .  \nDespite the challenges of atmospheric complexity and data accuracy, weather forecasts remain indispensable for safety planning, industry operations, and economic decisions [1] . However, traditional systems for weather prediction and AQI estimation rely on conventional meteorological models, historical data archives, and realtime monitoring techniques [1] . These systems integrate various data sources such as historical meteorological records, real-time satellite imagery, ground-level measurements, and APIs like OpenWeather, Air Pollution, and Weather Map [1] .  \nTraditi","cbCaisU0D5Zz3itQ","https://ap.wps.com/l/cbCaisU0D5Zz3itQ","pdf",425218,1,9,"English","en",105,"# Introduction\n## Motivation and challenges in weather forecasting and AQI estimation\n## Traditional systems: strengths and limitations\n# Literature Survey\n## Comparative studies on machine learning for AQI and weather prediction","[{\"question\":\"What data sources does the proposed system use for weather prediction and AQI estimation?\",\"answer\":\"It integrates historical weather data with real-time atmospheric measurements from the OpenWeather API.\"},{\"question\":\"Which machine learning algorithm is used to construct the predictive models?\",\"answer\":\"The study uses the Random Forest machine learning algorithm to build prediction models.\"},{\"question\":\"How is the system deployed and delivered to users?\",\"answer\":\"The backend runs on a Django application hosted on AWS EC2 with Nginx as a reverse proxy, while the frontend is a ReactJS web app hosted on AWS S3 and delivered through CloudFront, with an additional mobile app for real-time updates.\"}]","Machine Learning-Based System for Weather Prediction and Air Quality Index Estimation - 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