Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping

Abstract

Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Kohat region, Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel-2 satellite data were utilized to generate an inventory map of groundwater using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater factors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region.

Keywords:

• Ground water potential zones
• GIS
• RS
• FR
• AUC

Acknowledgements

The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia.

Author contributions

Fakhrul Islam: methodology, software, formal analysis, visualization, data curation, writing—original draft, investigation, validation, writing—review and editing, Aqil Tariq: formal analysis, visualization, data curation, writing—review and editing, Supervision. Rufat Guluzade: writing—review and editing. Na Zhao: Funding, writing review and editing, Safeer Ullah Shah: data curation, writing—original draft, investigation, validation, writing—review and editing, Matee Ullah: writing—review and editing. Mian Luqman Hussain: writing—review and editing. Muhammad Nasar Ahmad: writing—review and editing, Abdulrahman Alasmari: writing—review and editing, Fahad M. Alzuaibr: writing—review and editing, Ahmad El Askary: writing—review and editing, Muhammad Aslam: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Data availability statement

Data available on the reasonable request from the 1^st author of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The Key Project of Innovation LREIS (KPI001). The authors would like to thank the university authority for financial support. The authors thanks to (TURSP-2020/82), Taif University, Taif, Saudi Arabia.