0% Complete
English
صفحه اصلی
/
پانزدهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Predicting Concentration of Particulate Matter (PM2.5) in Hamedan using Machine Learning Algorithms
نویسندگان :
Anita Karim Ghassabpour
1
Hatam Abdoli
2
Muharram Mansoorizadeh
3
Saeid Seyedi
4
1- دانشگاه بوعلی سینا
2- دانشگاه بوعلی سینا
3- دانشگاه بوعلی سینا
4- دانشگاه بوعلی سینا
کلمات کلیدی :
Air Pollution،Particulate Matter،PM2.5،Machine Learning،Hamedan
چکیده :
Given that fine particles are one of the main origins of respiratory disorders, it is considered that PM2.5 is among the important contributors to air pollution and is a serious global health concern nowadays. This paper considers a new analytical approach for the prediction of PM2.5 concentration in Hamadan, Iran, with hopes of finding some ways to reduce the negative impacts of air pollution. During the last two years, the PM2.5 hourly data was gathered; they were preprocessed, and the outlier values were imputed using K-Nearest Neighbors techniques. To increase the accuracy, the estimation was improved by applying four machine learning models, namely, random forest, decision tree, support vector machine, and linear regression. Originality is represented by merging machine learning models with the time series model ARIMA. Thus, each model hybrid takes the strengths from all, giving a higher value of prediction of PM2.5 concentration. In this study many metrics such as MSE, RMSE, MAE, precision, and recall are applied for finding out the best model performance. Probably the most relevant outcome of our results is that the combination of linear regression and ARIMA returned a significant performance boost: MSE improved by 58%, while RMSE improved by 35%. This dramatic improvement underlines the predictive potential of hybrid models for air quality forecasting and forms a milestone in the study of PM2.5 prediction for the region.
لیست مقالات
لیست مقالات بایگانی شده
Multi-Modal Longitudinal Tooth Labeling with Temporal Graph–Transformer Integration
Maral Mirza mohammadi - Mahdi Tarom
Integrating Wasserstein GANs for High-Speed Transformer-Based Neural Machine Translation
Parisa Nekoogol - Mostafa Salehi
A perceptual loss for screen content image super-resolution
Hossein Sekhavaty-Moghadam - Marzieh Hosseinkhani - Dr Azadeh Mansouri
Binary water stream algorithm: a new meta-heuristic optimization technique
Faezeh Rahimi Sebdani - Mehdi Nasri
Face Recognition Based on Local Statistical Features and Artificial Neural Network
Mehdi Moghimi - Dr Hadi Grailu
Experimental analysis of automated negotiation agents in modeling Gaussian bidders
Fatemeh Hassanvand - Dr Faria Nassiri-Mofakham
مدل یادگیری عمیق با بازنمایی چند مقیاسی زمان برای پیشبینی آبشار اطلاعاتی در شبکههای اجتماعی
مبینا پناهی - مهدی عمادی
مکانیابی خطاهای کاربردها و خدمات نرمافزاری با کمک تولید داده آزمون با نامتغیرهای محتمل
محمد نصرتی مقدم - حسن حقیقی - مجتبی وحیدی اصل
OENMOP: Loss-Aware 4×4 and 5×5 and Scalable Non‑blocking Optical Switches Designed for Odd-Even Routing Algorithm for Chip-Scale Interconnection Networks
Negin Bagheri Renani - Elham Yaghoubi - Mina Mohammadirad
Energy-Saving for User-Centric Dynamic 5G HetNets Using DRL Method
Erfan Rasti - Mohammad Ali Arami - Abbas Mohammadi
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.2