Spatial-Temporal Forecasting of Air Pollution in Saudi Arabian Cities Based on a Deep Learning Framework Enabled by AI.

Rafat Zrieq, Souad Kamel, Faris Al-Hamazani, Sahbi Boubaker, Rozan Attili, Marcos J Araúzo-Bravo

Abstract read

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Article in Toxics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Rafat ZrieqDepartment of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il 55471, Saudi Arabia.ORCID 0000-0001-8586-7583

Souad KamelDepartment of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia.

Faris Al-HamazaniDepartment of Health Informatics, College of Public Health and Health Informatics, University of Ha'il, Ha'il 55471, Saudi Arabia.

Sahbi BoubakerDepartment of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia.ORCID 0000-0003-4504-6929

Rozan AttiliDepartment of Medical Laboratory Science, Faculty of Pharmacy and Medical Science, Hebron University, Hebron P700, Palestine.ORCID 0000-0003-4555-3074

Marcos J Araúzo-BravoComputational Biology and Systems Biomedicine, Biogipuzkoa Health Research Institute, 20014 San Sebastian, Spain.ORCID 0000-0002-3264-464X

Funding

Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia-project 946 number IFP-22 085 number IFP-22 085

6 · The paper itself

Abstract

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM

Indexed as

air pollutiondeep learning (DL)eXtreme Gradient Boosting (XGBoost)healthlong short-term memory (LSTM)modelingpredictionquality of liferandom forest (RF)Saudi Arabia

Identifiers

PMID40863957

DOI10.3390/toxics13080682

PMCPMC12390308

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.