A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco

Meliho, Modeste; Khattabi, Abdellatif; Mhammdi, Nadia

doi:10.1007/s12665-018-7844-1

Cited by 51 publications

(40 citation statements)

References 33 publications

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“…Our results indicated that the classes of <650 m, <5000 m, and >23,000 m with IoE = 1.64, 1.39, and 1.46, respectively, have the most impact on gully occurrence. These results are in agreement with Conoscenti et al and Maliho et al [28,33]. In the case of the lithology type, volcanic rocks (IoE = 1.73) and clay accumulations (1.6) showed a higher susceptibility to gully occurrence than other lithology units.…”

Section: Applying the Index Of Entropy (Ioe) Modelsupporting

confidence: 92%

“…Over the last decades, with the advent of powerful processor systems such as the GIS (geographic information system), a wide variety of methods have been tested to identify areas that are susceptible to gully erosion around the world, and have achieved very acceptable results [25,26]. These models can include (i) expert knowledge-based models such as the analytic hierarchy process (AHP) [27]; (ii) bivariate and statistical-based models such as the frequency ratio (FR) [28], conditional probability (CP) [29], evidential belief function (EBF) [17], information value (IV) [30], certainty factor (CF) [31], index of entropy (IoE) [32], logistic regression (LR) [33][34][35][36], weights-of-evidence (WOE) [37], and maximum entropy (ME) [27,38,39]; and, (iii) machine learning models such as artificial neural network (ANN) [40], boosted regression tree (BRT) [41], multivariate adaptive regression spline (MARS) [42], random forest (RF) [43], linear discriminant analysis (ADA) [44], naïve Bayes tree (NBTree) [45], and classification and regression trees (CART) [41].…”

Section: Introductionmentioning

confidence: 99%

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Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

et al. 2019

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Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province.

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Section: Applying the Index Of Entropy (Ioe) Modelsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

et al. 2019

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“…Examination of the relative importance of GECFs shows that slope, TPI, and elevation were the most important in the study area and corroborates [11][12][13]23]. Zabihi et al [13] tested three models (FR, WoE and IoE) to model GE in Iran and found that of 12 GECFs, elevation and LU/LC were the most important in their study area.…”

Section: Discussionmentioning

confidence: 62%

“…Zabihi et al [13] tested three models (FR, WoE and IoE) to model GE in Iran and found that of 12 GECFs, elevation and LU/LC were the most important in their study area. Meliho et al [12] used IV and FR for GESM in the Ourika watershed in Morocco, and they found that LU/LC and slope had the most influence on gully formation.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical models provide a general advantage of working with diverse types of independent variables, like continues, binary, and categorical data [5]. The most successful models may be: information value (IV) [5], conditional probability [11], frequency ratio (FR) [12], evidential belief function [3], index of entropy (IoE) [13], certainty factor [14], weights of evidence (WOE) [15], and logistic regression [16]. The performance of statistical models is low, however.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

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RETRACTED: Systematic review on gully erosion measurement, modelling and management: Mitigation alternatives and policy recommendations

Chakrabortty

Pal

2023

Geological Journal

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The problem of “gully erosion” is one of the severe “natural hazards” associated with large‐scale land degradation in various parts of the world. Therefore, accurate modelling is one of the crucial responsibilities in this study on gully erosion. But there are still a lot of gaps in our knowledge of gully erosion and its consequences. Particularly at the regional to continental scale, this is true. Gullying is hence often forgotten about in regulation and watershed management plans. The scientific community is becoming increasingly interested in gully erosion, yet many information gaps still need to be filled. In this research, we are trying to fill the mentioned gap with the help of the existing literature. Most gully erosion research has been done to estimate the susceptible zones and ultimately select the best model. In this research, a test application has been made by considering some important models. Then the “vulnerability assessment” was done with the help of “gully erosion, climate‐induced chemical weathering and socio‐economic variables” at the village level. The village‐level information has been used to determine the vulnerability in this region. This information is beneficial for future researchers to conduct gully erosion research in various parts of the world.

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A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco

Cited by 51 publications

References 33 publications

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

RETRACTED: Systematic review on gully erosion measurement, modelling and management: Mitigation alternatives and policy recommendations

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