The tradeoff between soil erosion protection and water consumption in revegetation: Evaluation of new indicators and influencing factors

Geoderma ◽  
2019 ◽  
Vol 347 ◽  
pp. 32-39 ◽  
Author(s):  
Daili Pan ◽  
Shiwei Yang ◽  
Yaqian Song ◽  
Xiaodong Gao ◽  
Pute Wu ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Influencing Factors ◽  
Water Consumption ◽  
Erosion Protection

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

scholarly journals Understanding Soil Erosion Protection Capabilities of Four Different Plants on Silty Soil

2020 ◽  
Vol 981 ◽  
pp. 032053
Author(s):  
R Gobinath ◽  
G P Ganapathy ◽  
A A Salunkhe ◽  
G Raja ◽  
E Prasath ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Silty Soil ◽  
Erosion Protection

Comparison and Analysis of Prediction Methods for Water Consumption per Ten Thousand Yuan Industrial Added Value

2014 ◽  
Vol 1073-1076 ◽  
pp. 2653-2658
Author(s):  
Zhi Jun Yan ◽  
Ming Yue Zhang ◽  
Chun Xiao Xu ◽  
Hai Tao Zhao ◽  
Yue Ping Tang ◽  
...  
Keyword(s):  
Water Management ◽  
Influencing Factors ◽  
Water Consumption ◽  
Management System ◽  
Added Value ◽  
Specific Situation ◽  
Water Efficiency ◽  
Analysis Method ◽  
Water Management System ◽  
Factors Analysis

Water consumption per ten thousand yuan industrial added value (WCPIAV) is the assessment indicator to implement the most stringent water management system to control water efficiency. This paper proposes trend analysis method, elasticity coefficient analysis method and influencing factors analysis method to predict WCPIAV in Jiangsu province, the experimental areas where implement the most stringent water management system. The results show that different methods predict well in different cities, influencing factors analysis method works better than the other two methods. An appropriate method should be selected depending on the specific situation.

Forestland change and soil erosion in karst watershed under the Grain to Green Project

2020 ◽  
Author(s):  
Siwen Feng ◽  
Hongya Wang ◽  
Hongyan Liu ◽  
Chenyi Zhu ◽  
Shuai Li
Keyword(s):  
Soil Erosion ◽  
Influencing Factors ◽  
Southwest China ◽  
Drainage Basin ◽  
Karst Area ◽  
Karst Region ◽  
Vegetation Growth ◽  
The Past ◽  
Pixel Scale ◽  
River Drainage Basin

<div>With the implementation of the Grain to Green Project, the vegetation growth in karst region in southwest China has increased. In order to explore whether the growth of trees can be sustained after artificial afforestation in karst area and the influence of the forestland change on soil erosion, the WaTEM/SEDEM model was used to simulate the 11 stages of annual soil erosion in the past 33 years in Chongan river drainage basin in Guizhou, and the dominant influencing factors of soil erosion change in the past 33 years were discussed based the pixel scale in this study. The results showed that the forestland increased in a fluctuating way after the conversion project, and the decrease of forestland was mainly caused by drought, especially in the area where the dolomites were distributed. Therefore, the change of forestland caused no significant improvement in soil erosion since the Grain to Green Project.</div><p><!--5f39ae17-8c62-4a45-bc43-b32064c9388a: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></p>

Assessment of agri-spillways as a soil erosion protection measure in Mediterranean sloping vineyards

2017 ◽  
Vol 14 (6) ◽  
pp. 1009-1022 ◽  
Author(s):  
Jesús Rodrigo-Comino ◽  
Stefan Wirtz ◽  
Eric C. Brevik ◽  
José D. Ruiz-Sinoga ◽  
Johannes B. Ries
Keyword(s):  
Soil Erosion ◽  
Protection Measure ◽  
Erosion Protection

scholarly journals Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jun Wang ◽  
Qian He ◽  
Ping Zhou ◽  
Qinghua Gong
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Influencing Factors ◽  
South China ◽  
Nature Reserve ◽  
Ground Truth ◽  
Landsat 8 ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
National Nature Reserve

The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m-resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.

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