Forecasting soil moisture on a spatial and temporal scale using machine learning algorithms

While the observations from earth-observing satellites and in-situ weather meteorological and monitoring stations continue to expand, researchers are to deal with an abundance of data and, consequently, with a long modeling procedure. Machine learning algorithms, as universal nonlinear function approximation tools, are effective in analyzing and modeling spatio-temporal environmental data, more efficiently, either in time or in terms of the availability of various variables, than physically-based models.

Soil moisture is an essential climatic parameter, especially for understanding and forecasting variations in surface temperature, precipitation, drought, flood, and the effects of climate change. As a parameter with high spatial and temporal variability, it is a strong necessity for predictive models that embed spatially irregular measurements, which stand for spatially distributed weather meteorological and monitoring stations. To date, most approaches, that have been documented in the literature, model environmental data only at the discrete locations of the monitoring stations.

This research aims to employ a recently proposed methodology, for spatio-temporal prediction of environmental data (Amato et al., 2020), and to propose a new methodology for spatio-temporal prediction of soil moisture in lowland areas, making use of the basic hydrologic premise that precipitation and temperature strongly hinge on topography. The features of the machine learning models used to predict soil moisture within the research area are the meteorological parameters of several agro-meteorological weather stations that have been installed at the site.

The study area is the plain of Arta, located in the Epirus region (NW Greece), and includes the lower part of the watersheds of rivers Aracthos and Louros. The final receptor for upstream surface water and groundwater is a sensitive and complex system of wetlands, the marine ecosystem of Amvrakikos. The research area is receiving a lot of attention because of its agricultural characteristics and water infrastructures (extensive irrigation and drainage network, pumping stations, and hydroelectric dam).

Acknowledgments: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call SUPPORT OF REGIONAL EXCELLENCE (project code MIS: 5047059).

Amato, F., Guignard, F., Robert, S., & Kanevski, M. (2020). A novel framework for spatio temporal prediction of environmental data using deep learning. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-79148-7