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Predicting sheet and rill erosion of Shihmen reservoir watershed in Taiwan using machine learning
(2019-07-01)
Nguyen, Kieu Anh
;
Chen, Walter
;
Lin, Bor Shiun
;
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 R<sup>2</sup>. 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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A Multiple Regression Analysis for Predicting Salinity in Shallow Groundwater
(2017-01-01)
Seeboonruang, Uma
Groundwater salinity is a severe problem particularly to agricultural lands. Measuring the water quality index at some particular locations might not be easy. The objective of this research is hence to predict the groundwater salinity, in terms of electrical conductivity (EC) in the shallow groundwater in the Northeast of Thailand. Groundwater EC was measured for the period of over 2 years at 14 different locations at different time intervals. The data was interpolated and analyzed for basic statistical properties including autocorrelation and stationarity. Linear regression model and transformed linear regression models were developed. The two models produced high adjusted R<sup>2</sup> about 0.8 during the calibration step. However, the transformed model provided a better accuracy during the verification step. This variation can be attributed to unaccounted factors, collinearity, and stationarity. The model can be applied to predict the groundwater salinity using the groundwater quality measured at some surrounding region.
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The effects of special events on regression for subcompact car sales in Thailand
(2016-11-01)
Rattanametawee, Witchaya
;
Leenawong, Chartchai
;
Netisopakul, Ponrudee
This research proposes a method to dealing with multiple linear regression that integrates the seasonality as well as the effects of some special or unanticipated events for sales figures. The method is then applied to the car sales figures in Thailand after having been through the 2011 national big flood and the 2011-2012 government’s initiative tax-incentive program for boosting the automobile industry. Besides Thailand’s Gross Domestic Products (GDP) and the 12-month Loan’s Interest Rate as explanatory variables, seasonal dummy variables along with the proposed special event variables and appropriate event tagging are incorporated. The statistical results obtained from the proposed regression model with seasons and events, compared to the models with neither seasons nor both yields highest adjusted coefficient of determination (R-squre) and accuracy (MAPE).
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Effects of wire-EDM machining variables on surface roughness of newly developed DC 53 die steel: Design of experiments and regression model
(2007-10-01)
Kanlayasiri, K.
;
Boonmung, S.
DC53 is a newly developed cold die steel from Daido Steel, Japan. It is an improvement over the familiar cold die steel SKD11. Because DC53 is a new die steel, only little information is available in literature for its machining characteristics. This paper presents an investigation of the effects of machining variables on the surface roughness of wire-EDMed DC53 die steel. In this study, the machining variables investigated were pulse-peak current, pulse-on time, pulse-off time, and wire tension. Analysis of variance (ANOVA) technique was used to find out the variables affecting the surface roughness. Assumptions of ANOVA were discussed and carefully examined using analysis of residuals. Quantitative testing methods on residual analysis were used in place of the typical qualitative testing techniques. Results from the analysis show that pulse-on time and pulse-peak current are significant variables to the surface roughness of wire-EDMed DC53 die steel. The surface roughness of the test specimen increases when these two parameters increase. Lastly, a mathematical model was developed using multiple regression method to formulate the pulse-on time and pulse-peak current to the surface roughness. The developed model was validated with a new set of experimental data, and the maximum prediction error of the model was less than 7%. © 2007.

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