Impact of storm water on groundwater recharge and discharge using machine learning

Surface water and groundwater are both important water sources for people’s livelihood, irrigation and industry. Especially, groundwater can supply water stability in times of drought to mitigate drought disaster. Due to the uneven temporal and spatial distribution of rainfall in Taiwan, there is a large difference in rainfall during the wet and dry seasons, and the rapid changes in the terrain slope cause the river flow rapid, which cannot store and utilize water resources. The rapid economic development makes the water demand increase year by year. Therefore, the surface water cannot meet the demand. Groundwater flows slow and replenishment is difficult. Long-term overuse will cause the gradual depletion of underground water sources, resulting in severe damages such as stratum subsidence and seawater intrusion. Therefore, if we can master the situation of groundwater changes and it is helpful to the management and deployment of surface water and groundwater resources.

The purpose of this study is to explore the interaction between surface water and groundwater during typhoon periods using machine learning methods. The research area is the Choshui river basin. According to the long-term monitoring of hydrological and groundwater data, different temporal and spatial distributions of rainfall events are selected to conduct principal component analysis (PCA) for each aquifer. Through the principal component weights, tempo-spatial distribution of rainfall and streamflow to analyze the trend of groundwater observation wells, and then use the recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to predict the hourly groundwater level. Principal component analysis was used to understand the impact of storm water on the groundwater recharge, and the relationship between principal component scores and flow and rainfall was used to find out the key factors affecting the groundwater level. The results can provide the sensitive areas of groundwater recharge in Choshui river basin for adopting the optimal water resource allocation strategy.

Keywords:Groundwater; Principal component analysis (PCA); Machine learning