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Author: search.filters.author.Chen, WalterSubject: search.filters.subject.Shihmen Reservoir watershed

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    Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
    (2021-01-01)
    Nguyen, Kieu Anh
    ;
    Chen, Walter
    ;
    Lin, Bor Shiun
    ;
    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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    Evaluation of the SEdiment Delivery Distributed (SEDD) model in the Shihmen Reservoir watershed
    (2020-08-01)
    Thomas, Kent
    ;
    Chen, Walter
    ;
    Lin, Bor Shiun
    ;
    Seeboonruang, Uma
    The sediment delivery ratio (SDR) connects the weight of sediments eroded and trans-ported 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 geospa-tial 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 prioritiza-tion in decision-making. Finally, it is essential to note that the sediment yield modeling can be im-proved by increased on-site validation and the use of aerial photogrammetry to deliver more up-dated data to better understand the field situations.
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    Using machine learning-based algorithms to analyze erosion rates of a watershed in Northern Taiwan
    (2020-03-01)
    Nguyen, Kieu Anh
    ;
    Chen, Walter
    ;
    Lin, Bor Shiun
    ;
    Seeboonruang, Uma
    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 Effciency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, theWilcoxon 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.