Volume 28, Issue 2 (Summer 2024)
jwss 2024, 28(2): 97-120 |
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Khajeh M, Komaki C B, Rezaei M, Sheikh V, Ebadi L. Assessment and Spatial Modeling of Land Subsidence Hazard Using the LiCSBAS Model and Random Forest Algorithm
(Case Study: Marvdasht Kharame Plain). jwss 2024; 28 (2) :97-120
URL: http://jstnar.iut.ac.ir/article-1-4390-en.html
URL: http://jstnar.iut.ac.ir/article-1-4390-en.html
Department of Arid Regions Management, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. , komaki@gau.ac.ir
Abstract: (604 Views)
In the future, the risk of land subsidence due to water resources shortage crisis and improper water resources management will become more and more dangerous. It is necessary to assess and identify areas susceptible to subsidence risk and take necessary actions to reduce risks related to land subsidence. In this study, first, the risk of land subsidence was identified and evaluated using a radar interferometry method called LiCSBAS. Then, the spatial relationship between the occurrence of land subsidence hazard and effective factors such as ground elevation, slope, slope aspect, lithology, land use, groundwater decline, distance from rivers, distance from faults, topographic moisture index, and arc curvature was investigated using the random forest (RF) model. In the end, the land subsidence hazard sensitivity map was prepared after calibrating the random forest algorithm. The analysis of LiCSBAS interferometric time series data from 2015 to 2022 showed that the center of the Marvdasht-Kharameh plain and adjacent agricultural areas are continuously subsiding and the mean deformation rate map showed a subsidence rate of 11.6 centimeters per year. The results of determining the spatial relationship between subsidence occurrence and effective factors confirmed the positive impact of distance from rivers, urban and agricultural land uses, depth of bedrock (aquifer thickness), groundwater decline, and alluvial and fine-grained formations on this phenomenon. Also, the results of subsidence modeling using the random forest algorithm showed that factors such as bedrock depth, groundwater decline, land use, and geology have the greatest impact on the potential for subsidence occurrence in the study area. Also, based on the results, about 3 to 4 percent of the areas are in the very high and extremely high-risk classes of land subsidence, especially in the center and suburbs of Mervdasht. Therefore, water resources management and control and developing a systematic program to reduce subsidence risk and aquifer recharge conservation in Merudasht-Kharameh Plain is essential.
Keywords: Risk assessment, Subsidence, LiCSBAS technique, Marvdasht-Kharameh plain, Random forest model
Type of Study: Research |
Subject:
Ggeneral
Received: 2023/10/2 | Accepted: 2024/04/8 | Published: 2024/08/31
Received: 2023/10/2 | Accepted: 2024/04/8 | Published: 2024/08/31
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