Forecast Salinity Changes in Coastal Wetland Using Deep Learning-based LSTM Model

Understanding and predicting salinity dynamics in marshlands is crucial for effective management and biodiversity conservation. Those ecosystems are facing increasing threats due to climate change and anthropogenic activities as they are located at the interface of terrestrial and aquatic environments. Here we present a cutting-edge study on the forecasting of salinity changes in St. Jones Reserve (39.10 ◦N, 75.44 ◦W) in Dover, DE utilizing a recurrent neural network-based Long Short-Term Memory (LSTM) model. Our overall objectives are to enhance the accuracy and robustness of salinity forecasts and facilitate informed decision-making for marshland conservation and sustainable land use. Model input features include measured groundwater and salinity levels as measured by Solinst 3001 Levelogger LTC 5 and 3001 Barologger 5 sensors at four sites along the transects. All the sensors collected data at a 15-minute interval from March 2022. The key contribution of this research includes the development of a novel LSTM architecture optimized marshland environments, as well as the integration of advanced feature engineering techniques to enhance model performance. The methodology is validated through extensive experimentation, utilizing historical salinity data from diverse marshland locations worldwide. Results demonstrate the model’s ability to outperform existing forecasting methods, providing timely and accurate predictions of salinity changes. Furthermore, the research explores the potential for real-time monitoring applications, aiding land managers and policymakers in implementing strategies to mitigate the impact of salinity fluctuations on marshland ecosystems.