Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m³/s, mean absolute error (MAE) of 29.034 m³/s, correlation coefficient (R) of 0.924, and determination coefficient (R²) of 0.817. However, when the amount of dam inflow is below 100 m³/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m³/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m³/s, MAE 18.063 m³/s, R 0.927, and R² 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m³/s, MAE 18.093 m³/s, R 0.912, and R² 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R²) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors.

Understanding dissolved organic carbon (DOC) fluxes in boreal freshwater systems is critical for constraining high‐latitude carbon budgets and anticipating climate‐driven changes. Here, we present a retrospective assessment (2000–2024) of DOC fluxes in the Upper Mackenzie River watershed, focusing on Great Slave Lake (GSL), a key hydrological node in North America's largest Arctic‐draining river system. Using field observations, remote sensing, and hydrological modeling, we quantify tributary DOC inputs to GSL, estimate DOC export through its outflow—the Mackenzie River—and construct a lake‐wide DOC budget. DOC fluxes exhibited pronounced spatial heterogeneity across tributaries, driven primarily by discharge and watershed characteristics such as wetland extent, slope, and soil organic carbon content. Wetland‐rich, low‐relief basins delivered the highest DOC concentrations, whereas Canadian Shield rivers contributed the lowest. Mass balance estimates indicated that the Slave River contributes ∼70% of the total flux to GSL, followed by the Hay River (∼10%), and GSL removed ∼30% of incoming DOC, functioning as a substantial DOC sink and modulator of downstream fluxes. The major tributaries supplying GSL also exhibited DOC chemical compositions distinct from the Mackenzie River outflow, indicating in‐lake transformations. This regulatory role helps explain previously reported differences between the Mackenzie River and major Siberian rivers that lack large lake influences. By altering oceanward DOC fluxes, GSL could potentially affect Arctic Ocean carbon cycling and the long‐term fate of terrigenous DOC. As boreal warm seasons intensify, the modulatory influence of GSL and large lakes is expected to increase, underscoring the need to explicitly represent lake processes in northern carbon assessments.