Document Type : Research Paper

Authors

• Zahra Khosravani ¹
• Mohammad Akhavan Ghalibaf ²
• Maryam Dehghani ³
• Vali Derhami ⁴
• Mustafa Bolca ⁵

¹ Ph.D. Student of Combat Desertification, Department of Arid Land and Desert Management, Faculty of Natural Resources and Desert Studies, Yazd University, Iran.

² Assistant Professor of Arid Land and Desert Management Department, Faculty of Natural Resources and Desert Studies, Yazd University, Iran.

³ Associated Professor of Civil and Environmental Engineering Department, School of Engineering, Shiraz University, Shiraz.

⁴ Professor of Computer Engineering Department, Faculty of Engineering, Yazd University, Iran.

⁵ Professor of Soil Science Department, Faculty of Agriculture, Ege University, Izmir, Turkey.

Abstract

The aim of this study was to model the subsidence of Abarkouh plain using inSAR and artificial intelligence techniques. At first, the subsidence map was prepared using the 46 Sentinel - 1 radar images (2014 – 2018) and radar interferometry techniques. Then, the Feedforward artificial neural network (ANN) algorithm was used to model the subsidence. In this algorithm, groundwater level changes (2014-2018), groundwater level, aquifer thickness, clay thickness in the aquifer and the clay thickness in the range of groundwater level changes (2014 - 2018) were introduced as input layers and the subsidence layer obtained from the radar interferometry method was introduced as an output layer to model training. These five parameters were obtained from the measured data set of 34 piezometer wells and 77 logs available in the archive of Regional Water Company of Yazd province. After initial checking of the data accuracy, the Kriging interpolation method was used to extend the five parameters to the whole region and the raster layers were prepared. The results of inSAR showed that maximum subsidence in parts of the studied area, i.e. in east, north - east and north, were 6, 2.7 and 1.6 cm/year respectively. Also, in order to verify the accuracy of the map resulting from using a neural network model, it was compared with the map with the radar imaging method. For this purpose, model evaluation criteria such as Nash-Sutcliffe (NS), RMSE,,MAE)and MARE were used, which 0.9524, 0.0018, 0.0012 and 0.1545 were obtained respectively.

Keywords

• groundwater
• fine-grained sediments
• artificial neural network
• Sentinel-1 radar images
• subsidence

20.1001.1.50442008.1401.75.3.7.5