Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Geoplanning : Journal of Geomatics and Planning
Nickbeen, Fatemeh
Unknown Affiliation
Earth & Planetary Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Groundwater Nitrate Modeling in Tehran Metropolis Using Artificial Neural Network and Kriging Methods Nickbeen, Fatemeh; Salmanmahiny, Abdolrassoul
Geoplanning: Journal of Geomatics and Planning Vol 11, No 2 (2024)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.11.2.177-188

Abstract

This study examined the relationship between groundwater quality and land use in Tehran. For this purpose, the possible relationship between the types of land uses and the concentration of nitrate in groundwater parameters was modelled using a Multi-Layer Perceptron (MLP) artificial neural network in geographic information system (GIS). The optimal network model was selected based on the mean root mean square error (RMSE) and correlation coefficient. Interpolation through Kriging was also performed to compare its results with those of the predicted model derived from an artificial neural network. The results showed that the neural network has a high capability for predicting and modelling groundwater nitrate concentration compared to the Kriging method. The high accuracy (RMSE: 0.003) of the neural network makes it a useful tool in relevant management issues. Our results of network sensitivity analysis were similar to scientific findings regarding the factors influencing the formation of nitrate in groundwater. Model outputs in the form of maps, tables, and graphs allowed the study of the role of each variable and the extent of its impact on groundwater quality. Performing various simulations and modelling of groundwater pollution provides an effective benchmark towards optimizing the management, control, planning, and decision-making in urban areas and can lead to economic and environmental savings.
Co-Authors Salmanmahiny, Abdolrassoul