InSAR-Deep learning approach for simulation and prediction of land subsidence in arid regions

Massive groundwater pumping for agricultural and industrial activities results in significant land subsidence in the arid world. In an acute water crisis, monitoring land subsidence and its key drivers is essential to assist groundwater depletion mitigation strategy. Physical models for aquifer simulation related to land deformation are computationally expensive. The interferometric synthetic aperture radar (InSAR) technique provides precise deformation mapping yet is affected by tropospheric and ionospheric errors. This study explores the capabilities of the deep learning approach coupled with satellite-derived variables in modeling subsidence, spatially and temporally, from 2016 to 2020 and predicting subsidence in the near future by using a recurrent neural network (RNN) in the Shabestar basin, Iran. The basin is part of the Urmia Lake River Basin, embracing 6.4 million people, yet has been primarily desiccated due to the over-usage of water resources in the basin. The deep learning model incorporates InSAR-derived land subsidence and its satellite-based key drivers such as actual evapotranspiration, Normalized Difference Vegetation Index (NDVI), land surface temperature, precipitation to yield the importance of critical drivers to inform groundwater governance. The land deformation in the area varied between -93.2 mm/year to 16 mm/year on average in 2016-2020. Our findings reveal that precipitation, evapotranspiration, and vegetation coverage primarily affected land subsidence; furthermore, the subsidence rate is predicted to increase rapidly. The phenomenon has the same trend with the variation of the Urmia Lake level. This study demonstrates the potential of artificial intelligence incorporating satellite-based ancillary data in land subsidence monitoring and prediction and contributes to future groundwater management.