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Article

Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models

1
Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea
2
Korea Water Environment Research Institute, Chuncheon-si 24408, Korea
3
Department of Biological Environment, Kangwon National University, Chuncheon-si 24341, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Su-Chin Chen
Water 2021, 13(3), 382; https://doi.org/10.3390/w13030382
Received: 17 December 2020 / Revised: 27 January 2021 / Accepted: 28 January 2021 / Published: 1 February 2021
(This article belongs to the Special Issue Soil–Water Conservation, Erosion, and Landslide)
Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R2) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors. View Full-Text
Keywords: rainfall erosivity factor; USLE R; machine learning; Deep Neural Network rainfall erosivity factor; USLE R; machine learning; Deep Neural Network
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MDPI and ACS Style

Lee, J.; Lee, S.; Hong, J.; Lee, D.; Bae, J.H.; Yang, J.E.; Kim, J.; Lim, K.J. Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models. Water 2021, 13, 382. https://doi.org/10.3390/w13030382

AMA Style

Lee J, Lee S, Hong J, Lee D, Bae JH, Yang JE, Kim J, Lim KJ. Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models. Water. 2021; 13(3):382. https://doi.org/10.3390/w13030382

Chicago/Turabian Style

Lee, Jimin, Seoro Lee, Jiyeong Hong, Dongjun Lee, Joo H. Bae, Jae E. Yang, Jonggun Kim, and Kyoung J. Lim. 2021. "Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models" Water 13, no. 3: 382. https://doi.org/10.3390/w13030382

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