Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

Abstract

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.

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

“…The R 2 value indicates the consistency with which the predicted values versus the measured values following a regression line [27]. It ranges from zero to one.…”

“…A total of 14 environmental factors were utilized as the predictors (independent factors or input variables) in the model, namely 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. These factors have been gathered from different sources and become a geospatial database, as described in Nguyen et al [27].…”

“…This step was repeated three times (i.e., Grouping #1, Grouping #2, and Grouping #3) to reduce the data variability to sampling. Because stratified random sampling has been shown to produce better outcomes than the simple random sampling [27], only stratified random sampling was used in this study to ensure the proper representation of the population. In stratified random sampling, the dataset was divided into several strata, and each stratum was sampled proportionately (70/30).…”

“…The Wilcoxon signed-rank test was conducted to determine if a statistically significant difference exists between the three models. Finally, the results of ANN, ANFIS, and SVM were tabulated and compared with results from our previous research [27].…”

“…In the field of soil erosion modeling, there have been many studies carried out to estimate soil erosion using models such as USLE [9,19], SWAT [20,21], WEPP [22,23], WaTEM/SEDEM [24,25], and EUROSEM [26]. However, there has been no application of ML algorithms to estimate soil erosion except for in our previous work [27], in which we used Decision Tree (DT), Random Forest (RF), and Multiple Regression (MR) to create ML models to predict the soil erosion depths (rates). Therefore, this study aims to extend the investigation to the application of other ML techniques.…”