Integrating physical modeling with artificial intelligence for predicting fish survival zones in polluted rivers to maintain a sustainable aquaculture industry
Hussein Karam Abd El-Sattar, Mohammed Elshambakey, Ahmed Saleh, Samar Antar · 2026
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Description
Hussein Karam Abd El-Sattar and co-authors present a 2026 journal article on predicting where fish can survive in polluted rivers. Fish farms need clean, oxygen-rich water, but real river data are often scarce. The team blended physics-based modeling with artificial intelligence to fill that gap.
First they solved equations for how pollution and oxygen move through a river, creating fifty thousand simulated readings. Then they trained a hybrid program that pairs a random forest with a neural network. It learned to map survival zones and to find the slowest water flow and lowest starting oxygen that stay safe. Against nine rival programs, the hybrid scored near-perfect accuracy and trained far faster than the slowest rival. The authors offer it as a planning tool for farm siting.
Catalog record
| CREATOR | Hussein Karam Abd El-Sattar, Mohammed Elshambakey, Ahmed Saleh, Samar Antar |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 204568 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |