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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
(2021-01-01)
Nguyen, Kieu Anh
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.
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Impact of climate change on soil erosion in the lam phra phloeng watershed
(2020-12-01)
Sirikaew, Uba
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Seeboonruang, Uma
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Tanachaichoksirikun, Pinit
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Wattanasetpong, Jatuwat
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Chulkaivalsucharit, Virun
Soil erosion plays a vital role in reducing reservoir capacity. The Lam Phra Phloeng (LPP) dams were built for flood protection and irrigation. However, they have experienced reservoir sedimentation, and the capacity of the reservoir has decreased. The surrounding soil surface was easily eroded and transported by heavy rainfall and surface runoff to streams and eventually into the reservoir. Understanding this soil erosion and sedimentation is necessary for preventing further decline of reservoir capacity and water management. This research aims to estimate long-term average annual soil erosion and predict sediment yield in the reservoir due to climate change. The methodology is determined soil loss parameters and sediment yield using the Universal Soil Loss Equation (USLE) with the Sediment Delivery Ratio (SDR). The USLE and SDR methods differed from field data, with an average absolute error of 4.0%. The Global Climatic Model, Institute Pierre Simon Laplace-Climate Model version 5A (IPSL-CM5A-MR), with Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, was downscaled and analyzed to forecast future rainfall in the watershed. The high intensity of rainfall contributed to higher soil erosion, in RCP 8.5. Interestingly, the high and very high-risk areas increased, but the moderate risk area declined, indicating that the moderate risk area should be a priority in land management. However, the heavy rainfall and high slope gradient led to a slight increase in the soil erosion in some areas because the land covers were evergreen and deciduous forest. The prediction of sediment yield was positively correlated with the intensity of rainfall in the central part of the watershed, because the rainfall and runoff led the sediment to the river and streams, indicating that the land cover should be managed to prevent capacity decline.
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Predicting sheet and rill erosion of Shihmen reservoir watershed in Taiwan using machine learning
(2019-07-01)
Nguyen, Kieu Anh
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
;
Thomas, Kent
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.
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Soil erosion modeling and comparison using slope units and grid cells in Shihmen reservoir watershed in Northern Taiwan
(2018-10-03)
Liu, Yi Hsin
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Li, Dong Huang
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Chen, Walter
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Lin, Bor Shiun
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Seeboonruang, Uma
Soil erosion is a global problem that will become worse as a result of climate change. While many parts of the world are speculating about the effect of increased rainfall intensity and frequency on soil erosion, Taiwan's mountainous areas are already facing the power of rainfall erosivity more than six times the global average. To improve the modeling ability of extreme rainfall conditions on highly rugged terrains, we use two analysis units to simulate soil erosion at the Shihmen reservoir watershed in northern Taiwan. The first one is the grid cell method, which divides the study area into 10 m by 10 m grid cells. The second one is the slope unit method, which divides the study area using natural breaks in landform. We compared the modeling results with field measurements of erosion pins. To our surprise, the grid cell method is much more accurate in predicting soil erosion than the slope unit method, although the slope unit method resembles the real terrains much better than the grid cell method. The average erosion pin measurement is 6.5 mm in the Shihmen reservoir watershed, which is equivalent to 90.6 t ha-1 yr-1 of soil erosion.
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Identifying and comparing relatively high soil erosion sites with four DEMs
(2018-09-01)
Chen, Walter
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Li, Dong Huang
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Yang, Kai Jie
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Tsai, Fuan
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Seeboonruang, Uma
Soil loss due to sheet or rill soil erosion is a critical problem in watersheds of Taiwan. However, an order-of-magnitude discrepancy of soil loss in the literature raises many questions. In this study, we conducted a new analysis using the most recent available data and the Universal Soil Loss Equation (USLE) to compute the amounts of sheet and rill erosion of the Shihmen reservoir watershed in northern Taiwan. Using four different Digital Elevation Models (DEMs), we identified relatively high soil erosion sites and found them to be located at similar locations despite of the difference in DEM. We also determined that the average soil erosion in the Shihmen reservoir watershed is comparable to other watersheds in Asia, but higher than those of the European Union. Furthermore, soil erosion is not uniformly distributed throughout the study area. It is found that the distribution of soil erosion is highly skewed to the right (right-tailed), which means that the majority of the distribution is concentrated to the left side (many cells with low soil erosion). Based on our model, approximately 2% of the areas account for 30% of the soil erosion. In other words, a small proportion of the areas contribute to a large proportion of the total soil loss. Moreover, the DEM created from airborne LiDAR yields the highest amount of soil erosion, the two DEMs created from satellite images yield the lowest amounts of soil erosion, and the DEM created from aerial photographs yields an in-between soil erosion amount. Their vertical resolutions range from high to low. It appears that the amount of soil erosion is influenced by the vertical accuracy of DEMs. In addition to the comparison of DEMs, we demonstrated rudimentary steps to visualize areas of high soil erosion risk using freely available tool for long-term monitoring.

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