Dataset and models for "Modelling shallow groundwater level fluctuations in very flat landscapes based on satellite data and machine learning"
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This repository contains the data, trained models, and results from the Random Forest analysis to predict water table depth (WTD) in the Pampean Region, Argentina: Title: Modelling shallow groundwater level fluctuations in very flat landscapes based on satellite data and machine learning ## Authors Javier Houspanossian¹, Francisco Diez¹, Raul Rivas¹, Esteban Jobbagy², Mauro Holzman¹, Gabriëlle J. M. De Lannoy³ **Affiliations:**¹ Institute of Hydrology of Plains "Dr. Eduardo Jorge Usunoff" (IHLLA), CONICET – Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC PBA), Argentina. ² Group of Environmental Studies (GEA), National University of San Luis (UNSL) – CONICET, Argentina. ³ Remote Sensing and Data Assimilation research group, Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium. **Corresponding author:** Javier Houspanossian - jhouspa@gmail.com This repository contains the data, trained models, and results from the Random Forest analysis to predict water table depth (WTD) in the Pampean Region, Argentina. The optimal model (Full\_2) uses climatic variables (precipitation and evapotranspiration accumulated over multiple temporal windows) and the Surface Water Coverage Index (SWCI) as predictors.
