Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran

Kariminejad, Narges; Hosseinalizadeh, Mohsen; Pourghasemi, Hamid Reza; Bernatek‐Jakiel, Anita; Campetella, Giandiego; Ownegh, Majid

doi:10.1016/j.scitotenv.2019.04.306

Cited by 47 publications

(21 citation statements)

References 85 publications

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“…Gully erosion is a complex process that results from the interplay of numerous factors [106,107]. After reviewing gully erosion literature and considering local conditions and available data, we selected 12 topographic, hydrological, geological, and anthropogenic factors for inclusion in the modeling process.…”

Section: Gully Conditioning Factorsmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

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Section: Gully Conditioning Factorsmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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“…The orthoimages mosaic and digital elevation model (DEM) were produced through the 3D photo‐reconstruction applying multiview stereo and structure from motion in the Pix4D software. The details of flight surveys are given by Kariminejad et al (). The DEM with the resolution of 1 m was used to derive different topographic factors (Figure ), which were applied as inputs to the RF models.…”

Section: Methodsmentioning

confidence: 99%

“…In order to assess the susceptibility of GHs and PCs, the data set of 17 controlling factors was prepared. The variables considered in this study were chosen on the basis of literature review (Al‐Abadi & Al‐Ali, ; Bernatek‐Jakiel, Kacprzak, & Stolarczyk, ; Di Stefano, Ferro, Palmeri, & Pampalone, ; Gan, Jia, Shao, Guo, & Li, ; Kariminejad et al, ; Lucà, Conforti, & Robustelli, ). The topography was represented by seven variables obtained from the DEM derived from UAV images using the basic terrain analyses in SAGA‐GIS software.…”

Section: Methodsmentioning

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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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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“…Nevertheless, accurate inventories of mapped gullies, required for model training and validation, remain difficult and labour-intensive to construct, especially in the Global South (e.g. Mararakanye and Le Roux, 2012;Golosov et al, 2018;Guyassa et al, 2018;Kariminejad et al, 2019;Yibeltal et al, 2019). However, freely available tools such as Google Earth open up promising perspectives in this regard (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting gully densities at sub‐continental scales: a case study for the Horn of Africa

Vanmaercke

Chen

Haregeweyn

et al. 2020

Earth Surf Processes Landf

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Despite its environmental and scientific significance, predicting gully erosion remains problematic. This is especially so in strongly contrasting and degraded regions such as the Horn of Africa. Machine learning algorithms such as random forests (RF) offer great potential to deal with the complex, often non‐linear, nature of factors controlling gully erosion. Nonetheless, their applicability at regional to continental scales remains largely untested. Moreover, such algorithms require large amounts of observations for model training and testing. Collecting such data remains an important bottleneck. Here we help to address these gaps by developing and testing a methodology to simulate gully densities across Ethiopia, Eritrea and Djibouti (total area: 1.2 million km2). We propose a methodology to quickly assess the gully head density (GHD) for representative 1 km2 study sites by visually scoring the presence of gullies in Google Earth and then converting these scores to realistic estimates of GHD. Based on this approach, we compiled GHD observations for 1,700 sites. We used these data to train sets of RF regression models that simulate GHD at a 1 km2 resolution, based on topographic/geomorphic, land cover, soil and rainfall conditions. Our approach also accounts for uncertainties in GHD observations. Independent validations showed generally acceptable simulations of regional GHD patterns. We further show that: (i) model performance strongly depends on the amount of training data used, (ii) large prediction errors mainly occur in areas where also the predicted uncertainty is large and (iii) collecting additional training data for these areas results in more drastic model performance improvements. Analyses of the feature importance of predictor variables further showed that patterns of GHD across the Horn of Africa strongly depend on NDVI and annual rainfall, but also on normalized steepness index (ksn) and distance to rivers. Overall, our work opens promising perspectives to assess gully densities at continental scales. © 2020 John Wiley & Sons, Ltd.

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Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran

Cited by 47 publications

References 85 publications

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

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

Predicting gully densities at sub‐continental scales: a case study for the Horn of Africa

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