The influence of rainfall intensity on soil loss mass from cellular confined slopes

Назир, Рамли; Ghareh, Soheil; Mosallanezhad, Mansour; Moayedi, Hossein

doi:10.1016/j.measurement.2015.11.007

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…The reduction on the erodibility factor affects positively the ability of the soil to resist the action of aggressive forces of water (pluvial and fluvial) (Imani, et al, 2014). In this study was not denoted statistical significance of positive correlation between soil loss and erodibility factor (Table 3), due to the fact that, besides erodibility, soil loss is influenced by a large number of other factors, such as: rainfall intensity (Vaezi et al, 2017) as the key factor in the development of the water erosion process (Kadović, 1999), then the slope of the terrain (Kadović,1999;Nazir et al, 2016), land use (Vaezi et al, 2017 etc.…”

Section: Discussion / Diskusijamentioning

confidence: 68%

Effects of Ameliorative Afforestation on the Erodibility Factor and Soil Loss in the Grdelica Gorge

Baumgertel¹,

Lukić²,

Simić³

et al. 2020

GSFUBL

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The paper presents research carried out in the area of Grdelica gorge in 60 years old black pine (Pinusnigra Arnold.) stands. The aim of this study was to determine whether the applied planting method ofameliorative afforestation had an impact on physical and chemical soil properties, soil erodibility andsoil loss. The experimental fields were selected from the afforested areas where soil samples weretaken from fixed depths and then were subjected to physical and chemical laboratory analyses. Thesoil erodibility factor K was calculated using the Wischmeier and Smith formula, whilst, soil loss wasestimated according to Erosion Potential method – EPM by Gavrilović. According to the results of theresearch, in the studied period of 60 years there was a change in the soil physical and chemical properties.There was also a reduction in the soil erodibility factor and soil loss which can be the result ofameliorative afforestation. The afforestation method can affect the reduction of soil losses, while theerodibility factor may be affected indirectly (e.g. by increase in the organic matter content, formingstructural aggregates) not immediately after afforestation, but in a long term period.

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Section: Discussion / Diskusijamentioning

confidence: 68%

Effects of Ameliorative Afforestation on the Erodibility Factor and Soil Loss in the Grdelica Gorge

Baumgertel¹,

Lukić²,

Simić³

et al. 2020

GSFUBL

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“…Many believe that this is because of the rapid expansion of economy and the improvement of living requirements. Currently, energy required for buildings accounts for almost 40% of all energy use in Europe [3]. Some reports have indicated that in countries such as United States and China, this value accounts for about 39% of the whole energy demand along with 27.5% of nationally consumed energy.…”

Section: Introductionmentioning

confidence: 99%

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

et al. 2019

Self Cite

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The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

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“…Moreover, they cannot be used as a comprehensive solution due to their limitation in investigating a specific slope condition (e.g., slope angle, height, groundwater level, soil properties, etc.). Different types of limit equilibrium methods (LEMs), finite element model (FEM), and numerical solutions have been extensively employed for the engineering problem [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

et al. 2019

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In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

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The influence of rainfall intensity on soil loss mass from cellular confined slopes

Cited by 21 publications

References 8 publications

Effects of Ameliorative Afforestation on the Erodibility Factor and Soil Loss in the Grdelica Gorge

Effects of Ameliorative Afforestation on the Erodibility Factor and Soil Loss in the Grdelica Gorge

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

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