Developing groundwater potentiality models by coupling ensemble machine learning algorithms and statistical techniques for sustainable groundwater management-Reference-Cited by-同舟云学术

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Developing groundwater potentiality models by coupling ensemble machine learning algorithms and statistical techniques for sustainable groundwater management

Published:2021-10-19 Issue: Volume: Page:1-27
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Mallick Javed¹,Naikoo Mohd Waseem²,Talukdar Swapan²³,Ahmed Ishita Afreen³,Rahman Atiqur³,Islam Abu Reza Md. Towfiqul⁴^ORCID,Pal Swades³,Ghose Bonosri⁴,Shashtri Satyanarayan⁵

Affiliation:

1. Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia

2. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India

3. Department of Geography, University of Gour Banga, Malda, India

4. Department of Disaster Management, Begum Rokeya University, Rongpur, Bangladesh

5. Meteorological & Environmental Science, School of Applied Sciences, Fiji National University, Fiji

Funder

Deanship of Scientific Research at King Khalid University

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1987535

Reference134 articles.

1. Quantitative analysis of soil erosion causative factors for susceptibility assessment in a complex watershed

2. Mapping of Groundwater Recharge Potential Zones and Identification of Suitable Site-Specific Recharge Mechanisms in a Tropical River Basin

3. Groundwater Use for Irrigation and its Productivity: Status and Opportunities for Crop Intensification for Food Security in Bangladesh

4. Predicting spatiotemporal changes of channel morphology in the reach of Teesta River, Bangladesh using GIS and ARIMA modeling

5. Inhomogeneous continuum random trees and the entrance boundary of the additive coalescent

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