Title : ( Karst Groundwater Potential Mapping Using Machine Learning Model )
Authors: Hossein Mohammadzadeh , Jafar Hashemi , Hamid Ghalibaf Mohammad Abadi ,
Full TextAbstract
The objective of this paper is to present a groundwater potential zoning map for the Hezar Masjid highlands, located northeast of Mashhad, using the Random Forest (RF) machine learning model. The zoning map was developed based on the locations of 1,438 springs in the area and 16 factors influencing groundwater potential. The model\\\\\\\\\\\\\\\'s performance was assessed using various statistical criteria, including the area under the receiver operating characteristic (ROC) curve (AUC = 0.93), indicating excellent accuracy.
Keywords
, Machine learning , groundwater Potential , Hezar Masjed , Karst , Random Forest
@inproceedings{paperid:1102013,
author = {Mohammadzadeh, Hossein and Hashemi, Jafar and Ghalibaf Mohammad Abadi, Hamid},
title = {Karst Groundwater Potential Mapping Using Machine Learning Model},
booktitle = {دومین سمپوزیوم منطقه ای نوآوری در علم وفناوری},
year = {2025},
location = {مشهد, IRAN},
keywords = {Machine learning ، groundwater Potential ، Hezar Masjed ، Karst ، Random Forest},
}
%0 Conference Proceedings
%T Karst Groundwater Potential Mapping Using Machine Learning Model
%A Mohammadzadeh, Hossein
%A Hashemi, Jafar
%A Ghalibaf Mohammad Abadi, Hamid
%J دومین سمپوزیوم منطقه ای نوآوری در علم وفناوری
%D 2025
