This page has only limited features, please log in for full access.

Mr. Kent Thomas
National Taipei Univerity of Technology

Basic Info


Research Keywords & Expertise

0 Civil Engineering
0 Sediment Transport
0 Sedimentation
0 landslides
0 soil erosion

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 02 August 2020 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

Kent Thomas; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed. Sustainability 2020, 12, 6221 .

AMA Style

Kent Thomas, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang. Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed. Sustainability. 2020; 12 (15):6221.

Chicago/Turabian Style

Kent Thomas; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang. 2020. "Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed." Sustainability 12, no. 15: 6221.

Journal article
Published: 17 June 2020 in Sustainability
Reads 0
Downloads 0

Sediment transport to river channels in a basin is of great significance for a variety of reasons ranging from soil preservation to siltation prevention of reservoirs. Among the commonly used models of sediment transport, the SEdiment Delivery Distributed model (SEDD) uses an exponential function to model the likelihood of eroded soils reaching the rivers and denotes the probability as the Sediment Delivery Ratio of morphological unit i (SDRi). The use of probability to model SDRi in SEDD led us to examine the model and check for its statistical validity. As a result, we found that the SEDD model had several false assertions and needs to be revised to correct for the discrepancies with the statistical properties of the exponential distributions. The results of our study are presented here. We propose an alternative model, the Revised SEDD (RSEDD) model, to better estimate SDRi. We also show how to calibrate the model parameters and examine an example watershed to see if the travel time of sediments follows an exponential distribution. Finally, we reviewed studies citing the SEDD model to explore if they would be impacted by switching to the proposed RSEDD model.

ACS Style

Walter Chen; Kent Thomas. Revised SEDD (RSEDD) Model for Sediment Delivery Processes at the Basin Scale. Sustainability 2020, 12, 1 .

AMA Style

Walter Chen, Kent Thomas. Revised SEDD (RSEDD) Model for Sediment Delivery Processes at the Basin Scale. Sustainability. 2020; 12 (12):1.

Chicago/Turabian Style

Walter Chen; Kent Thomas. 2020. "Revised SEDD (RSEDD) Model for Sediment Delivery Processes at the Basin Scale." Sustainability 12, no. 12: 1.

Journal article
Published: 01 July 2019 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

Kieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Kent Thomas. Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning. Sustainability 2019, 11, 3615 .

AMA Style

Kieu Anh Nguyen, Walter Chen, Bor-Shiun Lin, Uma Seeboonruang, Kent Thomas. Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning. Sustainability. 2019; 11 (13):3615.

Chicago/Turabian Style

Kieu Anh Nguyen; Walter Chen; Bor-Shiun Lin; Uma Seeboonruang; Kent Thomas. 2019. "Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning." Sustainability 11, no. 13: 3615.

Journal article
Published: 11 January 2019 in Sustainability
Reads 0
Downloads 0

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.

ACS Style

Bor-Shiun Lin; Chun-Kai Chen; Kent Thomas; Chen-Kun Hsu; Hsing-Chuan Ho. Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed. Sustainability 2019, 11, 355 .

AMA Style

Bor-Shiun Lin, Chun-Kai Chen, Kent Thomas, Chen-Kun Hsu, Hsing-Chuan Ho. Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed. Sustainability. 2019; 11 (2):355.

Chicago/Turabian Style

Bor-Shiun Lin; Chun-Kai Chen; Kent Thomas; Chen-Kun Hsu; Hsing-Chuan Ho. 2019. "Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed." Sustainability 11, no. 2: 355.

Journal article
Published: 06 January 2015 in Paddy and Water Environment
Reads 0
Downloads 0

This study compiles the latest regional topographic data from field investigation and remote-sensing images to recalculate parameters of the universal soil loss equation (USLE) model of the Shenmu watershed; also to compensate for reduced accuracy of this model on small-scale slopes, this study incorporates soil erosion pin data which were collected periodically to measure the extent of soil erosion. Firstly, this study utilized the USLE model and soil erosion pin data to compare the soil erosion potential of the Chushui and Aiyuzi subwatersheds and concluded that soil erosion drastically increased if accumulated rainfall exceeded 200 mm; also, erosion depths were greater in the Aiyuzi subwatershed while estimated total erosion volume was higher in the Chushui subwatershed; this was attributed to the larger area of Chushui subwatershed and based on field measurements which supported the results of the USLE model. Secondly, this study utilized modified USLE model to compare the extreme event erosion resulting from typhoon Morakot which revealed that high rainfall intensity and long-duration rainfall events can generate large volume non-point sources of sediment that is estimated to far exceed 7–10 times of the annual soil erosion. Thirdly, this study related the C parameter of the USLE model to the existing land use in the Shenmu watershed using current, real data. Finally, this study established a post-typhoon Morakot soil erosion risk map composed of five categories of risk which was compared with post-event land cover to suggest high-erosion risk zones that may require further monitoring, remediation, and engineering measures to limit soil loss.

ACS Style

B. S. Lin; K. Thomas; C. K. Chen; H. C. Ho. Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters. Paddy and Water Environment 2015, 14, 19 -43.

AMA Style

B. S. Lin, K. Thomas, C. K. Chen, H. C. Ho. Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters. Paddy and Water Environment. 2015; 14 (1):19-43.

Chicago/Turabian Style

B. S. Lin; K. Thomas; C. K. Chen; H. C. Ho. 2015. "Evaluation of soil erosion risk for watershed management in Shenmu watershed, central Taiwan using USLE model parameters." Paddy and Water Environment 14, no. 1: 19-43.