Using Simple LSTM Models to Evaluate Effects of a River Restoration on Groundwater in Kushiro Wetland, Hokkaido, Japan

Abstract

Wetland ecosystems with proper functioning provide various ecosystem services. Therefore, their conservation and restoration are of fundamental importance for sustainable development. This study used a deep learning model for groundwater level prediction to evaluate a wetland restoration project implemented in the Kushiro Wetland in Japan. The Kushiro Wetland had been degraded due to river improvement work. However, in 2010, a wetland restoration project was carried out to restore the meandering river channel, and a decade has passed since its completion. In this study, the wetland restoration project was evaluated by comparing the response of the groundwater level using a model that reproduced physical conditions with different characteristics before and after the restoration. At first, a deep learning model was created to predict groundwater levels pre- and post-restoration of a meandering river channel using observation data. Long short-term memory (LSTM) was used as the deep learning model. The most important aspect of this study was that LSTM was trained for each of the pre- and post-restoration periods when the hydrological and geological characteristics changed due to the river channel’s restoration. The trained LSTM model achieved high performance with a prediction error of the groundwater levels within 0.162 m at all observation points. Next, the LSTM models trained with the observation data of the post-restoration period were applied to evaluate the effectiveness of the meandering channel restoration. The results indicated that the meandering channel restoration improved hydrological processes in groundwater levels, i.e., their rainfall response and average groundwater water levels. Furthermore, the variable importance analysis of the explanatory variables in the LSTM model showed that river discharge and precipitation significantly contributed to groundwater level recovery in the Kushiro Wetland. These results indicated that the LSTM model could learn the differences in hydrological and geological characteristics’ changes due to channel restoration to groundwater levels. Furthermore, LSTM is a data-driven deep learning model, and by learning hydrological and geological conditions to identify factors that may affect groundwater levels, LSTM has the potential to become a powerful analysis method that can be used for environmental management and conservation issues.