• Energy Research

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

The sediment delivery ratio (SDR) connects the weight of sediments eroded and transported from slopes of a watershed to the weight that eventually enters streams and rivers ending at the watershed outlet. For watershed management agencies, the estimation of annual sediment yield (SY) and the sediment delivery has been a top priority due to the influence that sedimentation has on the holding capacity of reservoirs and the annual economic cost of sediment-related disasters. This study establishes the SEdiment Delivery Distributed (SEDD) model for the Shihmen Reservoir watershed using watershed-wide SDRw and determines the geospatial distribution of individual SDRi and SY in its sub-watersheds. Furthermore, this research considers the statistical and geospatial distribution of SDRi across the two discretizations of sub-watersheds in the study area. It shows the probability density function (PDF) of the SDRi. The watershed-specific coefficient (β) of SDRi is 0.00515 for the Shihmen Reservoir watershed using the recursive method. The SY mean of the entire watershed was determined to be 42.08 t/ha/year. Moreover, maps of the mean SY by 25 and 93 sub-watersheds were proposed for watershed prioritization for future research and remedial works. The outcomes of this study can ameliorate future watershed remediation planning and sediment control by the implementation of geospatial SDRw/SDRi and the inclusion of the sub-watershed prioritization in decision-making. Finally, it is essential to note that the sediment yield modeling can be improved by increased on-site validation and the use of aerial photogrammetry to deliver more updated data to better understand the field situations.

Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.

The estimation of soil erosion in Taiwan and many countries of the world is based on the widely used universal soil loss equation (USLE), which includes the factor of soil erodibility (K-factor). In Taiwan, K-factor values are referenced from past research compiled in the Taiwan Soil and Water Conservation Manual, but there is limited data for the downstream area of the Shihmen reservoir watershed. The designated K-factor from the manual cannot be directly applied to large-scale regional levels and also cannot distinguish and clarify the difference of soil erosion between small field plots or subdivisions. In view of the above, this study establishes additional values of K-factor by utilizing the double rings infiltration test and measures of soil physical–chemical properties and increases the spatial resolution of K-factor map for Shihmen reservoir watershed. Furthermore, the established values of K-factors were validated with the designated value set at Fuxing Sanmin from the manual for verifying the correctness of estimates. It is found that the comparative results agree well with established estimates within an allowable error range. Thus, the K-factors established by this study update the previous K-factor system and can be spatially estimated for any area of interest within the Shihmen reservoir watershed and improving upon past limitations.