Using machine learning algorithms to map the groundwater recharge potential zones-Reference-Cited by-同舟云学术

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Using machine learning algorithms to map the groundwater recharge potential zones

Published:2020-07 Issue: Volume:265 Page:110525
ISSN:0301-4797
Container-title:Journal of Environmental Management
language:en
Short-container-title:Journal of Environmental Management

Author:

Pourghasemi Hamid Reza,Sadhasivam Nitheshnirmal,Yousefi Saleh,Tavangar Shahla,Ghaffari Nazarlou Hamid,Santosh M.

Funder

Shiraz University

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal,General Medicine,Environmental Engineering

Reference129 articles.

1. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions;Abdollahi;Bull. Eng. Geol. Environ.,2019

2. Remote sensing and GIS based groundwater potential and recharge zones mapping using multi-criteria decision making technique;Agarwal;Water Resour. Manag.,2016

3. Assessing the suitability of soft computing approaches for forest fires prediction;Aljanabi;Applied Computing and Informatics,2018

4. Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions;Alfarrah;Water,2018

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3. Effects of DEM resolution and application of solely DEM-derived indicators on groundwater potential mapping in the mountainous area;Journal of Hydrology;2024-06

4. Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones;The Egyptian Journal of Remote Sensing and Space Sciences;2024-06

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