[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118700-en":3,"doc-seo-118700-105":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},118700,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Forecasting PM2.5 Concentrations in Chiang Mai using Machine Learning Models","Particulate matter 2.5 threatens human health and has driven rapid growth in research on forecasting PM2.5 levels. The study evaluates three widely used machine learning models—artificial neural network (ANN), long short-term memory (LSTM), and convolutional neural network (CNN)—to predict PM2.5 concentrations in Chiang Mai. Raw data from Thailand’s Pollution Control Department cover January 2014 to June 2023, totaling 3,468 observations. Data are divided into training, validation, and test sets, and median absolute error measures performance. Results show comparable PM2.5 behavior across models, with ANN achieving better error performance.","International Journal on Robotics, Automation and Sciences  \nForecasting PM2.5 Concentrations in Chiang Mai using Machine Learning Models  \nManlika Ratchagit*  \nAbstract – Particulate matter 2.5 poses a significant threat to human life. Over the past decade, there has been a significant increase in the number of articles dedicated to studying and forecasting PM2.5 concentrations. Thailand, particularly Chiang Mai, has elevated levels of dangerous PM2.5 throughout the hot season. The primary objective of this study is to evaluate the efficacy of three widely used machine learning models, namely artificial neural network (ANN), long short-term memory network (LSTM), and convolutional neural network (CNN), in predicting the levels of PM2.5 particles in Chiang Mai. The raw data are obtained from the Pollution Control Department, Ministry of Natural Resources and Environment Thailand between January 2014 and June 2023, a total of 3,468 observations. We split the data into three sets namely, training, validation, and test sets. The criterion to evaluate three machine learning techniques is the median absolute error. The experimental results confirm that all three machine learning models provide similar movements of PM2.5 dust pollution. Moreover, the artificial neural network technique provides better results than the others regarding error measurement.  \nKeywords— PM 2.5 Concentrations, Machine Learning, Artificial Neural Network, Long Short-Term Memory Network, Convolutional Neural Network.  \nI. INTRODUCTION  \nOne of the most important issues facing society today is air pollution. Air pollution commonly encompasses several forms such as particulate  \nmatter, ground-level ozone, automobile pollutants, and other pollutants that have long-term impacts on both human health and the ecosystem. The investigation of particulate matter, particularly particulate matter 2.5 (PM2 .5), was conducted ten years ago. The characteristics of PM2 .5 particles are their small diameter, light weight, high reactivity, and capacity to float and stay suspended in the environment for long periods of time. The presence of suspended particles can lead to a reduction in air visibility, hence causing haze conditions. Additionally, these particles can influence the radiation balance and the Earth's biological cycle [1] . The human risk significantly affects individuals' physical and mental well-being. Two primary issues that PM2 .5 can lead to are respiratory disorders (such as asthma, bronchitis, and chronic obstructive pulmonary disease, or COPD) and cardiovascular disorders (including heart attacks, strokes, arrhythmias, and heart disease) . PM2.5 can have long-term negative impacts on health, including death, a greater risk of lung function, and reduced lung function and development [2] . The presence of PM2 .5 in the environment has been found to have detrimental effects on various aspects, including the degradation of materials and structures, the deposition of acid, and the elevation of ozone levels [3] . Chiang Mai, Thailand, is ready for a PM2 .5 disaster area because, during the first week of April 2024, the average concentration of fine particulate matter smaller than 2.5 microns exceeded 150 micrograms per cubic  \n*Corresponding [author. Email: manlika@mju.ac.th](author. Email: manlika@mju.ac.th) ORCID: 0000-0001-8600-5387  \nManika Ratchagit*, Assistant Professor, Program in Statistics and Information Management , Faculty of Science, Maejo University, Chiang Mai 50290, Thailand.  \nInternational Journal on Robotics, Automation and Sciences (2024) 6, 2:37-41  \nmeter (μg/m3), which is extremely dangerous [4] . Chiang Mai is the capital city of the northern part of Thailand. High PM2 .5 levels in the northern region have caused numerous residents to seek medical assistance for respiratory ailments such as asthma and inflammation. According to a report by Maharaj Nakorn Chiang Mai Hospital on March 19, 2024, 30,339 people sought medical attention for re","cbCaivDkOc72ANI3","https://ap.wps.com/l/cbCaivDkOc72ANI3","pdf",496164,1,5,"English","en",105,"# Introduction\n## Air pollution and health impacts of PM2.5\n## Motivation and study objective\n# Literature Review\n## Prior statistical and deep learning forecasting studies\n## Comparative performance of forecasting methods","[{\"question\":\"Which machine learning models are evaluated for PM2.5 forecasting in Chiang Mai?\",\"answer\":\"The study compares artificial neural network (ANN), long short-term memory (LSTM), and convolutional neural network (CNN) for predicting PM2.5 concentrations.\"},{\"question\":\"What dataset and time range are used in the experiments?\",\"answer\":\"The raw data come from Thailand’s Pollution Control Department and cover January 2014 to June 2023, yielding 3,468 observations.\"},{\"question\":\"How is model performance measured and what is the main finding?\",\"answer\":\"Performance is evaluated using median absolute error. All three models show similar PM2.5 movement patterns, and ANN provides better error measurements than the others.\"}]","Forecasting PM2.5 Concentrations in Chiang Mai using Machine Learning Models | PDF",1785684955,13,{"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},"forecasting-pm25-concentrations-in-chiang-mai-using-machine-learning-models","",{"@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/forecasting-pm25-concentrations-in-chiang-mai-using-machine-learning-models/118700/",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},"Which machine learning models are evaluated for PM2.5 forecasting in Chiang Mai?","Question",{"text":75,"@type":76},"The study compares artificial neural network (ANN), long short-term memory (LSTM), and convolutional neural network (CNN) for predicting PM2.5 concentrations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and time range are used in the experiments?",{"text":80,"@type":76},"The raw data come from Thailand’s Pollution Control Department and cover January 2014 to June 2023, yielding 3,468 observations.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured and what is the main finding?",{"text":84,"@type":76},"Performance is evaluated using median absolute error. 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