Assessment of land subsidence using interferometric synthetic aperture radar time series analysis and artificial neural network in a geospatial information system: case study of Rafsanjan Plain

Citation

Bagheri M, Dehghani M, Esmaeily A & Akbari V (2019) Assessment of land subsidence using interferometric synthetic aperture radar time series analysis and artificial neural network in a geospatial information system: case study of Rafsanjan Plain. Journal of Applied Remote Sensing, 13 (04), p. 1. https://doi.org/10.1117/1.jrs.13.044530

Abstract
Land subsidence resulting from groundwater extraction is a widely recurring phenomenon worldwide. To assess land subsidence, traditional methods such as numerical and finite element methods have limitations due to the complex interactions between the different constructor factors of aquifer in each area. We produced a groundwater-induced subsidence map by applying the geological and hydrogeological information of the aquifer system using an artificial neural network (ANN) combined with interferometric synthetic aperture radar (InSAR) and geospatial information system. The main problem with neural networks is providing the ground-truth dataset for training step. Therefore, the subsidence rate used as the network output was estimated using the InSAR time series analysis method. This study indicates the ANN approach is capable of knowing the mechanism of the land subsidence and can be used as a complementary of InSAR method to estimate the land subsidence with effective parameters and accessible data such as groundwater-level data especially in those areas in which measuring the subsidence was not feasible using InSAR. However, the results indicated that average groundwater depth and groundwater level decline were the most effective factors influencing subsidence in the study area using sensitivity analysis.

Keywords
Interferometric synthetic aperture radar; Neurons; Artificial neural networks; Time series analysis; Data modeling; Neural networks; Geology

Journal
Journal of Applied Remote Sensing: Volume 13, Issue 04