The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration-Reference-Cited by-同舟云学术

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Published:2021-05 Issue: Volume:37 Page:100837
ISSN:2212-0955
Container-title:Urban Climate
language:en
Short-container-title:Urban Climate

Author:

Shams Seyedeh Reyhaneh,Jahani Ali,Kalantary Saba,Moeinaddini Mazaher,Khorasani Nematollah

Publisher

Elsevier BV

Subject

Atmospheric Science,Urban Studies,Environmental Science (miscellaneous),Geography, Planning and Development

Reference52 articles.

1. Development of ozone prediction model in urban area;Abdullah;Int. J. Innov. Technol. Explor. Eng.,2019

2. The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils;Akan;SN Appl. Sci.,2019

3. Evaluation of relationship between meteorological parameters and air pollutant concentrations during winter season in Elazığ, Turkey;Akpinar;Environ. Monit. Assess.,2008

4. Evaluation the effect of green space on air pollution dispersion using satellite images and landscape metrics: a case study of Isfahan City;Bagheri;Fresenius Environ. Bull.,2017

5. Integration assessment of the protected areas using landscape ecological approach (Case Study: Kolah Ghazy National Park and Wildlife Refuge);Barati;J. Town Country Plann.,2017

Cited by 94 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development of an automated photolysis rates prediction system based on machine learning;Journal of Environmental Sciences;2025-05

2. Short-term air quality prediction using point and interval deep learning systems coupled with multi-factor decomposition and data-driven tree compression;Applied Soft Computing;2024-11

3. Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model;Indoor Environments;2024-09

4. Changes in urban residential and agricultural sources induce a decrease in PM10 levels in cold clean area: A thirteen-year monitoring at the Longfengshan WMO/GAW regional background station in Northeast China;Atmospheric Environment;2024-09

5. Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM;Plant Methods;2024-08-20