Volume 24, Issue 74 (9-2024)
jgs 2024, 24(74): 76-97 |
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kiani S, mazidi A, Hosseini S Z A. (2024). Identification of subsidence areas using radar interferometry (Case study: Damaneh plain). jgs. 24(74), 76-97. doi:10.61186/jgs.24.74.14
URL: http://jgs.khu.ac.ir/article-1-3992-en.html
URL: http://jgs.khu.ac.ir/article-1-3992-en.html
1- Yazd University, Yazd University , arashkiani97rs@mail.com
2- Yazd University, Yazd University
2- Yazd University, Yazd University
Abstract: (3609 Views)
Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of 1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.
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This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)