Water Erosion in Different Slope Lengths on Bare Soil

Bagio, Bárbara; Bertol, Ildegardis; Wolschick, Neuro Hilton; Schneiders, Danieli; Santos, Maria Aparecida do Nascimento dos

doi:10.1590/18069657rbcs20160132

Cited by 28 publications

(17 citation statements)

References 31 publications

(41 reference statements)

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“…If we group the erosion pins according to the type of slopes, it can be seen from Table 3 that they are close to evenly represented in the data. The bare slopes, with the condition that represents soil without vegetation and the absence of protection on the slope, tend to have higher amounts of soil mobilization and transport by water [29]. The average erosion depth is the highest for this group with 8.38 mm/yr.…”

Section: Identification Of Important Factorsmentioning

confidence: 99%

Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

Nguyen

Chen

Lin³

et al. 2019

Sustainability

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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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Section: Identification Of Important Factorsmentioning

confidence: 99%

Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

Nguyen

Chen

Lin³

et al. 2019

Sustainability

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“…Three topographical factorsslope length, gradient, and shapeare important drivers of erosion due to their influences on the speed and volume of runoff (Bagio et al 2017). The DEM provided basic land surface data regarding slope, aspect, curvature, drainage area, drainage networks, and topographic indices (Mukherjee et al 2013).…”

Section: Topographical Factorsmentioning

confidence: 99%

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

Avand

Mohammadi

Mirchooli

et al. 2021

Preprint

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Despite advances in artificial intelligence modelling, the lack of soil erosion data and other watershed information is still one of the important factors limiting soil-erosion modelling. Additionally, the limited number of parameters and the lack of evaluation criteria are major disadvantages of empirical soil-erosion models. To overcome these limitations, we introduce a new approach that integrates empirical and artificial intelligence models. Erosion-prone locations (erosion ≥16 tons/ha/year) are identified using RUSLE model and a soil-erosion map is prepared using random forest (RF), artificial neural network (ANN), classification tree analysis (CTA), and generalized linear model (GLM). This study uses 13 factors affecting soil erosion in the Talar watershed, Iran, to increase prediction accuracy. The results reveal that the RF model has the highest prediction performance (AUC=0.95, Kappa=0.87, Accuracy=0.93, and Bias=0.88), outperforming the three machine-learning models. The results show that slope angle, land use/land cover, elevation, and rainfall erosivity are the factors that contribute the most to soil erosion propensity in the watershed. Curvature and topography position index (TPI) were removed from the analysis due to multicollinearity with other factors. The results can be used to improve the identification of hot spots of soil erosion, especially in watersheds for which soil-erosion data are limited.

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“…The effect of slope length on erosion occurs through an increase in the volume and the speed of runoff, resulting in increased capacity of the runoff to disaggregate and transport sediments (Bagarello and Ferro 2010). The research of (Bagio et al 2017) revealed that the increase in erosion with the length of the slope is explained by the greater erosive power of surface runoff, determined mainly by the increase in the volume and speed of runoff (Bagio et al 2017). Therefore, the longer the slope length, the higher the flow velocity and the greater the runoff volume.…”

Section: Introductionmentioning

confidence: 99%

The Role Of Relative Slope Length In Flood Hazard Mapping Using Ahp And Gis (Case Study: Lam River Basin, Vietnam)

Nguyen

Minh²,

Ahmad

et al. 2020

GES

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In addition to the five main factors affecting the formation of floods including slope, rainfall, drainage density, soil, and land cover, the relative slope length factor has also been considered to be one of the fundamental causes that contribute to flood hazard. The paper analyzes the theoretical basis for choosing the relative slope length criterion when zoning flood hazard in Lam river basin. The important role of this factor was evaluated by the results of the flood risk zoning map established by the method of integrating AHP and GIS technology in two cases: using 5 flood influence criteria and using 6 flood influence criteria. Flood hazard zoning maps for 2 cases were tested with 3 historic floods occurring on Oct 2010, Oct 2013 and Oct 2016. The results showed that the map established with six influence factors is more detailed and accurate than the one created with five factors affecting flood hazard because of the similarity with the reality of that map. The results of the study are applicable to other river basins which their geographical features are similar to characteristics the Lam river basin.

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Water Erosion in Different Slope Lengths on Bare Soil

Cited by 28 publications

References 31 publications

Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

A New Approach For Smart Soil Erosion Modelling: Integration of Empirical And Machine Learning Models

The Role Of Relative Slope Length In Flood Hazard Mapping Using Ahp And Gis (Case Study: Lam River Basin, Vietnam)

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