FORECASTING DEFORMATION TRIGGERED BY GROUNDWATER EXTRACTION USING PS-InSAR TIME SERIES. APPLYING MACHINE LEARNING AND STATISTICAL MODELS IN THE MADRID AQUIFER (SPAIN).

Ground deformation caused by groundwater exploitation leads to significant socio-economic losses worldwide. Driving factors such as population growth and climate change will increase these losses, especially in arid regions where droughts are becoming more intense, longer lasting, and frequent. Therefore, there is a need to generate models capable of forecasting ground deformation. However, few studies have analyzed deformation time series (DTS) to identify and characterize subsidence phenomena.

Our research aims to predict the ground deformation associated with groundwater abstractions in 18 wells of the Madrid Detrital Aquifer (ATDM) using statistical models and shallow and deep Machine Learning (ML) algorithms. We generated a database with 18 monthly time series (one for each well) between 1992 and 2010, with data for two variables: a binary variable indicating extraction-recovery cycles of the aquifer and a continuous variable representing the average deformation for the area of influence of each well. DTS generated from Persistent Scatter Interferometry (PSI) of ERS-1/2 and ENVISAT radar images were used to calculate the average deformation. Finally, we applied six different methods for forecasting DTS: two statistical models, Autoregressive Integrated Moving Average (ARIMA) and Prophet (P), one ensemble shallow ML algorithm, Random Forest (RF), one hybrid method, Neural Prophet (NP), and two Deep Learning (DL) techniques 1D Convolutional Neural Networks (CNN1D), and Long Short-Term Memory (LSTM).

The analysis of DTS allowed us to differentiate two zones with different hydrological behavior: a zone of higher permeability (north zone) and another of lower permeability (south zone). We found that establishing the architectures of ML and DL algorithms based on hydrological zones improves the prediction of ground deformation. ML and DL algorithms provide better forecasts compared to statistical and hybrid models. Specifically, LSTM and RF offer the best results. Our results show the potential of LSTM algorithms and the previous grouping of DTS in predicting ground deformation associated with groundwater exploitation.

This work has been developed thanks to the pre-doctoral grant for the Training of Research Personnel (PRE2021-100044) funded by MCIN/AEI/10.13039/501100011033 and by "FSE invests in your future" within the framework of the SARAI project "Towards a smart exploitation of land displacement data for the prevention and mitigation of geological-geotechnical risks" PID2020-116540RB-C22 funded by MCIN/AEI/10.13039/501100011033.