Exploring multi-pollution variability in the urban environment: geospatial AI-driven modeling of air and noise-Reference-Cited by-同舟云学术

Exploring multi-pollution variability in the urban environment: geospatial AI-driven modeling of air and noise

Published:2024-07-24 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Razavi-Termeh Seyed Vahid¹,Sadeghi-Niaraki Abolghasem¹,Jelokhani-Niaraki Mohammadreza²,Choi Soo-Mi¹

Affiliation:

1. Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea

2. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

Funder

ITRC Support Program

Metaverse Support Program to Nurture the Best Talents

Ministry of Science and ICT of Korea

Institute of Information and Communications Technology Planning and Evaluation

Ministry of Trade, Industry and Energy and Korea Institute for Advancement of Technology

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2378819

Reference138 articles.

1. Spatiotemporal Change of Air-Quality Patterns in Hubei Province—A Pre- to Post-COVID-19 Analysis Using Path Analysis and Regression

2. Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

3. Exploring the Combined Association between Road Traffic Noise and Air Quality Using QGIS

4. A Spatial Decision Support Approach for Flood Vulnerability Analysis in Urban Areas: A Case Study of Tehran

5. Developing vehicular traffic noise prediction model through ensemble machine learning algorithms with GIS