Resumen
Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the following research questions: (a) how will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs)?: (b) do machine learning models perform well enough in predicting changes of the groundwater in Denmark? If so, which ML model outperforms for forecasting these changes? Three ML algorithms were used in R: artificial neural networks (ANN), support vector machine (SVM) and random forest (RF). The ML models were trained with time-series data of groundwater levels taken at wells in the Hovedstaden region, for the period 1990?2018. Several independent variables were used to train the models, including different soil parameters, topographical features and climatic variables for the time period and region selected. Results show that the RF model outperformed the other two, resulting in a higher R-squared and lower mean absolute error (MAE). The future prediction maps for the different scenarios show little variation in the water table. Nevertheless, predictions show that it will rise slightly, mostly in the order of 0?0.25 m, especially during winter. The proposed approach in this study can be used to visualize areas where the water levels are expected to change, as well as to gain insights about how big the changes will be. The approaches and models developed with this paper could be replicated and applied to other study areas, allowing for the possibility to extend this model to a national level, improving the prevention and adaptation plans in Denmark and providing a more global overview of future water level predictions to more efficiently handle future climate change scenarios.