Geocarto International, Volume (37), No (26), Year (2022-12) , Pages (14065-14087)

Title : ( A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning )

Authors: Mahdi Panahi , Khabat Khosravi , Ali Golkarian , Mahsa Roostaei , Rahim Barzegar , Ebrahim Omidvar , Fatemeh Rezaie , Patricia M. Saco , Alireza Sharifi , Changhyun Jun , Sayed M. Bateni , Chang-Wook Lee , Saro Lee ,

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Abstract

Land subsidence (LS), which mainly results from poor watershed management, is a complex and nonlinear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road or fault, rainfall, geology and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANN and GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR deformation map, 39%, 20%, 25%, 13% and 3% of the investigated areas are subject to a yearly LS of 1 to 2.5, 2.5 to 5, 5 to 7.5, 7.5 to 10 and 10 to 20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km2) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future.

Keywords

Land subsidence; InSAR; GMDH; machine; learning; Iran
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@article{paperid:1090762,
author = {Mahdi Panahi and Khosravi, Khabat and Golkarian, Ali and Mahsa Roostaei and Rahim Barzegar and Ebrahim Omidvar and Fatemeh Rezaie and Patricia M. Saco and Alireza Sharifi and Changhyun Jun and Sayed M. Bateni and Chang-Wook Lee and Saro Lee},
title = {A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning},
journal = {Geocarto International},
year = {2022},
volume = {37},
number = {26},
month = {December},
issn = {1010-6049},
pages = {14065--14087},
numpages = {22},
keywords = {Land subsidence; InSAR; GMDH; machine; learning; Iran},
}

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%0 Journal Article
%T A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning
%A Mahdi Panahi
%A Khosravi, Khabat
%A Golkarian, Ali
%A Mahsa Roostaei
%A Rahim Barzegar
%A Ebrahim Omidvar
%A Fatemeh Rezaie
%A Patricia M. Saco
%A Alireza Sharifi
%A Changhyun Jun
%A Sayed M. Bateni
%A Chang-Wook Lee
%A Saro Lee
%J Geocarto International
%@ 1010-6049
%D 2022

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