An application of different summary statistics for modelling piping collapses and gully headcuts to evaluate their geomorphological interactions in Golestan Province, Iran

Hosseinalizadeh, Mohsen; Kariminejad, Narges; Alinejad, Mohammad

doi:10.1016/j.catena.2018.08.005

Cited by 18 publications

(9 citation statements)

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“…Therefore, the susceptibility modelling of natural hazards is based on the determination and analyses of factors controlling these hazards (e.g., Costanzo, Rotigliano, Irigaray, Jiménez‐Perálvarez, & Chacón, ; Pourghasemi, Yousefi, Kornejady, & Cerdà, ). This study has confirmed the spatial aggregation between GHs and PCs (Table ), which is in agreement with the recent report on the clustered spatial pattern of GHs and PCs determined by bivariate summary statistics in NE Iran (Hosseinalizadeh, Kariminejad, & Alinejad, ) and with other reports on the relationships of GHs and PCs (e.g., Bernatek‐Jakiel & Wrońska‐Wałach, ; Crouch, ; Frankl et al, ; Nichols et al, ; Parker & Higgins, ).…”

Section: Discussionsupporting

confidence: 92%

“…Even if a relatively small part of the study area (in this case 15%) is susceptible to both soil erosion features, that is, GHs and PCs (Figure ), the agricultural lands are threatened and the land management is difficult, especially when the PCs occur quickly (as reported for instance by Nichols et al, ) and may achieve a great depth. In NE Iran, the mean PCs depth is 2.5–3 m and the diameter ranges from 0.3 to 40 m (Hosseinalizadeh, Kariminejad, & Alinejad, , Hosseinalizadeh, Kariminejad, Rahmati, et al, ).…”

Section: Discussionmentioning

confidence: 99%

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GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran

et al. 2019

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When the effect of two soil erosion features, in this study, gully headcuts (GHs) and pipe collapses (PCs), is investigated jointly, a new comprehensive concept related to their controlling factors can be found. The objective of this paper to evaluate susceptibility to these two features in the hilly region of the Golestan Province (NE Iran), which may be helpful in land management and the sustainable development of the region. The maps of the controlling factors of GHs and PCs were constructed and the random forest algorithm was used to prioritize the factors controlling the occurrence of these two soil erosion features. The GHs and PCs susceptibility maps were prepared using the random forest model in the R software and validated applying the receiver operating characteristic curves, fourfold plot, and Cohen's kappa index, whereas the susceptibility map of the two soil erosion features was prepared by overlapping these maps. The results of factor importance analysis have indicated that land use, slope, and silt content are the most important factors in the occurrence of PCs, whereas slope gradient, silt content, and distance to streams are the most important in GHs occurrence. This means that steep and uncultivated slopes in the study area are most susceptible to GHs and PCs. The susceptibility models of GHs and PCs have excellent accuracy, that is, the area under the receiver operating characteristic curve values of GHs and PCs models were 0.960 and 0.935, respectively. The susceptibility map of GHs and PCs analysed jointly has shown that 66% of the area is not susceptible to any of these soil erosion features, whereas 15% is susceptible to both of them. The places susceptible to the two types of soil erosion features (GHs and PCs) indicate the areas where there is a high probability of GHs retreat due to the PCs. This study has confirmed that it is feasible to forecast the spatial behaviour of GHs and PCs occurrence and development. This is particularly preferred when the developed methods are applied for the susceptibility evaluation under various mitigation scenarios.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran

et al. 2019

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“…Data mining models have been considered by various researchers as suitable approaches in soil erosion studies. Examples for the application of data mining models are provided by studies on gully erosion [28][29][30][31][32][33][34], piping erosion [35,36], and landslides [25,[37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Ouri

Golshan

Janizadeh

et al. 2020

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Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

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“…In areas where the lithological and soil structure is composed of fine and discrete elements, the mechanism of the formation of piping erosion is such that this erosion causes washing and removal of fine particles by mechanical leaching and creates the conditions for tunnel erosion problems. Moreover, in areas with high solutes, water infiltration and solute dissolution accelerate the formation of piping erosion forms, or, in other words, dissolution plays a supportive role in creating these forms [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…The soil erosion studies in Iran focused on different factors and scales, from watershed scale soil erosion behavior [29] till the control that some factors such as texture exert [30], to gully erosion [31], soil detachment [32], or the use of the Universal Soil Loss Equation [33]. Although the research on soil erosion in Iran is a key environmental issue, piping erosion is a topic which was not addressed until recently [24] and literature about it is scarce. Although piping is relevant in Iran and other semiarid lands in the world [14,34,35], there is a need to deeply understand the spatial distribution, the origin and development, and the proper management in order to prevent it.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Band

Janizadeh

Saha

et al. 2020

Land

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Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.

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An application of different summary statistics for modelling piping collapses and gully headcuts to evaluate their geomorphological interactions in Golestan Province, Iran

Cited by 18 publications

References 50 publications

GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran

GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

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