A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.

Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences.

The objectives of this study were to assess the climate change impacts on sea-level rise (SLR) and freshwater recharge rates and to investigate these SLR and freshwater recharge rates on seawater intrusion in coastal groundwater systems through the Saturated-Unsaturated Transport (SUTRA) model. The Gunsan tide gauge station data were used to project SLR based on polynomial regressions. Freshwater recharge rates were assumed as 10% of the projected annual precipitation under climate change. The Byeonsan2 groundwater monitoring well for seawater intrusion was selected for the study. A total of 15 scenarios, including the baseline period (2005–2015), were made based on SLR projections and estimated freshwater recharge rates. The changes in salinity relative to the baseline at the monitoring well for each scenario were investigated through the SUTRA model. From the scenario of 0.57 m SLR with a freshwater recharge rate of 0.0058 kg s⁻¹, the largest salinity increase (40.3%) was simulated. We concluded that this study may provide a better understanding of the climate change impacts on seawater intrusion by considering both SLR and freshwater recharge rates.