A PM2.5 Concentration Prediction in High-Cost and Low-Cost Wireless Sensor Networks Using Neural Networks DOI
Marko Marković,

Đorđe Nešković,

Lara Kašca

et al.

2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), Journal Year: 2023, Volume and Issue: unknown, P. 151 - 154

Published: Oct. 25, 2023

Due to its increasing impact on human health, air pollution is becoming a progressively important topic in modern society. Particulate matter with diameter of 2.5 μm cited as one the main pollutants. Thus, prediction concentration these particles presents very research topic. Therefore, this paper, we observed Deep Learning based spatial realized by using installed high-cost sensors and/or low-cost sensors, which are simulated. Based obtained analysis results, proposal was made employ completely or partially, distributed Neural Networks, instead currently used wireless sensor network for PM2.5 measuring. It shown that way can lower complexity, datasets and time training without loss (or even gain) quality.

Language: Английский

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models DOI Creative Commons
Abbas Pak,

Abdullah Kaviani Rad,

Mohammad Javad Nematollahi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 2, 2025

Language: Английский

Citations

3
Long-Term Retrospective Predicted Concentration of PM2.5 in Upper Northern Thailand Using Machine Learning Models DOI Creative Commons
Sawaeng Kawichai, Patumrat Sripan, Amaraporn Rerkasem

et al.

Toxics, Journal Year: 2025, Volume and Issue: 13(3), P. 170 - 170

Published: Feb. 27, 2025

This study aims to build, for the first time, a model that uses machine learning (ML) approach predict long-term retrospective PM2.5 concentrations in upper northern Thailand, region impacted by biomass burning and transboundary pollution. The dataset includes PM10 levels, fire hotspots, critical meteorological data from 1 January 2011 31 December 2020. ML techniques, namely multi-layer perceptron neural network (MLP), support vector (SVM), multiple linear regression (MLR), decision tree (DT), random forests (RF), were used construct prediction models. best was selected considering root mean square error (RMSE), (MPE), relative (RPE) (the lower, better), coefficient of determination (R2) bigger, better). Our found model-based RF technique using PM10, CO2, O3, air pressure, rainfall, humidity, temperature, wind direction, speed performs when predicting concentration with an RMSE 6.82 µg/m3, MPE 4.33 RPE 22.50%, R2 0.93. this research could further studies effects on human health related issues.

Language: Английский

Citations

2
Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction DOI Creative Commons
Ming Wei,

Xiaopeng Du

Machine Learning with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 100624 - 100624

Published: Jan. 1, 2025

Language: Английский

Citations

1
Trend-attribute forecasting of hourly PM2.5 trends in fifteen cities of Central England applying optimized machine learning feature selection DOI
David A. Wood

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 356, P. 120561 - 120561

Published: March 12, 2024

Language: Английский

Citations

8
A novel ensemble machine learning method for accurate air quality prediction DOI
Murat Emeç, Mustafa Yurtsever

International Journal of Environmental Science and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: May 6, 2024

Language: Английский

Citations

7
Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction DOI
Sujan Ghimire, Ravinesh C. Deo, Ningbo Jiang

et al.

Atmospheric Environment, Journal Year: 2025, Volume and Issue: unknown, P. 121079 - 121079

Published: Feb. 1, 2025

Language: Английский

Citations

0
PM 2.5 Concentration 7-days Prediction in the Beijing-Tianjin-Hebei Region Using a Novel Stacking Framework DOI Creative Commons

Xintong Gao,

Xiaohong Wang,

Fuping Li

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract High-precision prediction of near-surface PM2.5 concentration is an significant theoretical prerequisite for effective monitoring and prevention air pollution, also provides guiding suggestions health risk control. In view the fact that control variables existing models are mostly dependent on influencing factors at near-surface, it often difficult to fully explore continuous spatio-temporal characteristics in PM2.5. this study, MODIS remote sensing-derived Aerosol Optical Depth (AOD) daily data, atmospheric environment ground station data meteorological introduced identify strong correlation factors. A highly robust seven-day model constructed based Stacking algorithm combined with various machine learning methods improve generalisation ability model; estimation integrated compared analyzed LSTM, RF KNN models. The results demonstrated basis RF-LSTM-Stacking exhibited a better fit, R², RMSE, MAE values 0.95, 7.74 µg/m³, 6.08 respectively. This approach improved accuracy by approximately 17% single model. Based was evident LSTM-RF model, fusion-based algorithm, significantly enhanced provided reference predicting early warning monitoring.

Language: Английский

Citations

0
Nationwide Machine Learning-Ensemble PM2.5 Mapping Prediction and Forecasting Models in South Korea with High Spatiotemporal Resolution and Health Risk Estimation-Based Evaluations DOI Creative Commons
Seoyeong Ahn, Ayoung Kim, Yeonseung Chung

et al.

Environment & Health, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

Citations

0
Long-term Prediction Method for PM2.5 Concentration Using Edge Channel Graph Attention Network and Gating Closed-form Continuous-time Neural Networks DOI
Chen Zhang, Xiaofan Li,

Hongyang Sheng

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 189, P. 356 - 373

Published: June 20, 2024

Language: Английский

Citations

2
PM2.5 Concentration Estimation Using Bi-LSTM with Osprey Optimization Method DOI Creative Commons

S. Saminathan,

C. Malathy

Nature Environment and Pollution Technology, Journal Year: 2024, Volume and Issue: 23(3), P. 1631 - 1638

Published: Sept. 1, 2024

Outdoor air pollution causes a lot of health problems for humans. Particulate Matter 2.5 (PM2.5), due to its small size, can enter the human respiratory system with ease and cause significant effects on This makes PM2.5 among various pollutants. Hence, it is important measure value accurately better management quality. Algorithms deep learning machine be used forecast quality data. A model that minimizes prediction error needed. In this paper, concentration estimation using Bi-LSTM (Bidirectional Long Short-Term Memory) meteorological data as predictor variables proposed. For values, hyperparameters are tuned Osprey Optimization Algorithm (OOA), recent meta-heuristic algorithm. The works optimal values identified by OOA performed than other models when they compared based evaluation metrics like Mean-Squared Error R2.

Language: Английский

Citations

2