Enhancing the prediction of groundwater quality index in semi-arid regions using a novel ANN-based hybrid arctic puffin-hippopotamus optimization model

Study region: The west of Minia Governorate, Egypt. Study focus: This study presents a novel hybrid arctic puffin–hippopotamus optimization (HPHO) algorithm combined with an artificial neural network (ANN) to improve irrigation water quality index (IWQI) predictions in semi-arid areas. A total of 88...

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Bibliographic Details
Main Authors:	Moustafa Gamal Snousy, Hussein M. Elshafie, Ashraf R. Abouelmagd, Najmaldin Ezaldin Hassan, Mahmoud E. Abd-Elmaboud, Ali Akbar Mohammadi, Ashraf M.T. Elewa, E. EL-Sayed, Ahmed M. Saqr
Format:	Article
Language:	English
Published:	Elsevier 2025-06-01
Series:	Journal of Hydrology: Regional Studies
Subjects:	Groundwater quality index Artificial neural networks Arctic puffin optimization Hippopotamus optimization Principal component analysis Sustainable development goals
Online Access:	http://www.sciencedirect.com/science/article/pii/S2214581825002496
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Enhancing the prediction of groundwater quality index in semi-arid regions using a novel ANN-based hybrid arctic puffin-hippopotamus optimization model

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