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Evaluating the performance of machine learning methods and Geographic Information System (GIS) in identifying groundwater potential zones in Tehran-Karaj plain, Iran by Vafadar, Saman (author);Rahimzadegan, Majid (author);Asadi, Roza (author) is a Environmental Science article available to read on EtoBox.

What is Evaluating the performance of machine learning methods and Geographic Information System (GIS) in identifying groundwater potential zones in Tehran-Karaj plain, Iran about?

Approaches based on Geographic Information Systems (GISs) are the most common and influential methods in identifying and mapping Groundwater Potential Zones (GWPZ). Moreover, employing Machine Learning (ML) algorithms in modeling the influencing parameters on GWPZ can improve the performance of these models. Therefore, the present study aims to assess the efficiency of ML methods, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGB) models, along with influencing factors on GWPZ in GIS environment. Hence, the Tehran-Karaj plain in Iran, with approximately 5156 km2 area and relatively diverse elevations (818 to 4374 m), including flat and mountainous areas, was selected as the study area. Information from 105 wells was collected and divided into 75% for training and 25% for validation of the models. The effects of twelve parameters were investigated in three categories: topography, hydrology, and vegetation/geology (four parameters for each item). Moreover, LR, RF, SVM, and XGB models were applied to calculate the scores of each influential factor and create maps of GWPZ for the study area. Results of assessing the ac

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Author

Vafadar, Saman (author);Rahimzadegan, Majid (author);Asadi, Roza (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)