A Novel Evolutionary Deep Learning Approach for PM<sub>2.5</sub> Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran

A Novel Evolutionary Deep Learning Approach for PM<sub>2.5</sub> Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran

Forecasting particulate matter with a diameter of 2.5 μm (PM<sub>2.5</sub>) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM<sub>2.5</sub> data, their limited numb...

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Bibliographic Details
Main Authors: Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:ISPRS International Journal of Geo-Information
Subjects:
PM<sub>2.5</sub>
aerosol optical depth
geographic information systems
remote sensing
long short-term memory
orchard algorithm
Online Access:https://www.mdpi.com/2220-9964/14/2/42
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