A hybrid artificial neural network-random forest model for enhanced wet bulb temperature prediction in arid urban environments
= 0.997) and reductions of up to 28.6% in error relative to the best standalone models. Overall, the study's findings demonstrate that hybrid machine learning models can deliver more accurate WBT predictions in arid regions, with direct implications for heat risk management, public health planning, and climate-resilient infrastructure design in rapidly warming arid cities.
Authors
- Saleh Alsulamy (ORCID: https://orcid.org/0000-0001-6991-1855)
- Abdullah Al Baky
- Ahmed Ali A. Shohan (ORCID: https://orcid.org/0000-0001-6339-2329)
- Tekalign Ketema Bahiru
- Al Hossain Rafi
- Abdulla Al Kafy
Institutions
- Khulna University of Engineering and Technology (BD)
- Taiwan Geographic Information Center (TW)
- United International University (BD)
- King Khalid University (SA)
- The University of Texas at Austin (US)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-04-26
- DOI
- https://doi.org/10.1038/s41598-026-43644-z
- Primary Topic
- Urban Heat Island Mitigation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deanship of Scientific Research, King Khalid University