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

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.

[1]  H. Pourghasemi,et al.  Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. , 2018, The Science of the total environment.

[2]  George N Zaimes,et al.  Assessing Riparian Conservation Land Management Practice Impacts on Gully Erosion in Iowa , 2012, Environmental Management.

[3]  Pijush Samui,et al.  A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. , 2019, The Science of the total environment.

[4]  T. Steenhuis,et al.  A Biophysical and Economic Assessment of a Community‐based Rehabilitated Gully in the Ethiopian Highlands , 2016 .

[5]  A. C. Imeson,et al.  Gully types and gully prediction. , 1980 .

[6]  J. Brice.  Erosion and deposition in the loess-mantled Great Plains, Medicine Creek drainage basin, Nebraska , 1966 .

[7]  W. Gburek.  Hydrology and the Management of Watersheds , 1998 .

[8]  A. Murwira,et al.  Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District – Zimbabwe , 2014 .

[9]  Christian Conoscenti,et al.  Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity , 2018 .

[10]  J. A. Gomez,et al.  A century of gully erosion research: Urgency, complexity and study approaches , 2016 .

[11]  Hamid Reza Pourghasemi,et al.  Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion , 2017 .

[12]  A. Cerda,et al.  Soil water erosion on road embankments in eastern Spain. , 2007, The Science of the total environment.

[13]  J. Poesen,et al.  Gully erosion and environmental change: importance and research needs , 2003 .

[14]  Saskia Keesstra,et al.  Assessment of soil particle erodibility and sediment trapping using check dams in small semi-arid catchments , 2017 .

[15]  Saro Lee,et al.  Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. , 2019, The Science of the total environment.

[16]  H. Pourghasemi,et al.  Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison , 2016, Natural Hazards.

[17]  Alireza Arabameri,et al.  GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. , 2019, The Science of the total environment.

[18]  J. Poesen,et al.  Contribution of gully erosion to sediment production in cultivated lands and rangelands , 1996 .

[19]  T. Lasanta,et al.  Soil erosion by piping in irrigated fields , 1997 .

[20]  A. Kornejady,et al.  Landslide susceptibility assessment using maximum entropy model with two different data sampling methods , 2017 .

[21]  H. Pourghasemi,et al.  GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique , 2018, Environmental Earth Sciences.

[22]  M. Conforti,et al.  Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy) , 2011 .

[23]  Amir Hamzeh Haghiabi,et al.  Forecasting flood-prone areas using Shannon’s entropy model , 2017, Journal of Earth System Science.

[24]  Abdellatif Khattabi,et al.  A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco , 2018, Environmental Earth Sciences.

[25]  P. Marín Sanleandro,et al.  The causes of piping in a set of abandoned agricultural terraces in southeast Spain , 2007 .

[26]  Biswajeet Pradhan,et al.  Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility. , 2019, The Science of the total environment.

[27]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[28]  Olivier Dewitte,et al.  Predicting the susceptibility to gully initiation in data-poor regions , 2015 .

[29]  C. Conoscenti,et al.  A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. , 2019, The Science of the total environment.

[30]  B. Pradhan,et al.  Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models , 2019, Geosciences Journal.

[31]  A. Arabameri.  Application of the Analytic Hierarchy Process ( AHP ) for locating fire stations : Case Study Maku City , 2014 .

[32]  Saskia Keesstra,et al.  Spatio-temporal variation of throughfall in a hyrcanian plain forest stand in Northern Iran , 2018 .

[33]  Wei Chen,et al.  Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models , 2019, Geoderma.

[34]  Biswajeet Pradhan,et al.  Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques , 2019, CATENA.

[35]  J. Poesen,et al.  Gully erosion: Impacts, factors and control , 2005 .

[36]  Jiarong Gao,et al.  Debris Flow Characteristics and Risk Degree Assessment in Changyuan Gully, Huairou District, Beijing , 2011 .

[37]  Mrinal K. Ghose,et al.  Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India , 2012, Arabian Journal of Geosciences.

[38]  Damien Raclot,et al.  Relative Contribution of Rill/Interrill and Gully/Channel Erosion to Small Reservoir Siltation in Mediterranean Environments , 2016 .

[39]  Hamid Reza Pourghasemi,et al.  Spatial modelling of gully erosion in Mazandaran Province, northern Iran , 2018 .

[40]  A. Jeyaram,et al.  Probabilistic Techniques, GIS and Remote Sensing in Landslide Hazard Mitigation: A Case Study from Sikkim Himalayas, India , 2005 .

[41]  Hassan Khosravi,et al.  Determination of sand dune characteristics through geomorphometry and wind data analysis in central Iran (Kashan Erg) , 2016, Arabian Journal of Geosciences.

[42]  Alireza Arabameri,et al.  Zoning Mashhad Watershed for Artificial Recharge of Underground Aquifers using TOPSIS Model and GIS Technique , 2015 .

[43]  B. H. Heede,et al.  MORPHOLOGY OF GULLIES IN THE COLORADO ROCKY MOUNTAINS , 1970 .

[44]  Hasan Ahmadi,et al.  Relationship between soil erosion, slope, parent material, and distance to road (Case study: Latian Watershed, Iran) , 2011 .

[45]  Hamid Reza Pourghasemi,et al.  Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R , 2019, Spatial Modeling in GIS and R for Earth and Environmental Sciences.

[46]  R. Muñoz-Salinas,et al.  Accurate automated assessment of gully cross‐section geometry using the photogrammetric interface FreeXSapp , 2018 .

[47]  Saskia Keesstra,et al.  The way forward: Can connectivity be useful to design better measuring and modelling schemes for water and sediment dynamics? , 2018, The Science of the total environment.

[48]  Tien-Chin Wang,et al.  Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR , 2009 .

[49]  I. D. Moore,et al.  Topographic Effects on the Distribution of Surface Soil Water and the Location of Ephemeral Gullies , 1988 .

[50]  Hubert Andrew Ireland,et al.  Principles of Gully Erosion in the Piedmont of South Carolina , 1939 .

[51]  Yufeng Shi,et al.  Landslide Stability Analysis Based on Generalized Information Entropy , 2009, ESIAT.

[52]  Biswajeet Pradhan,et al.  Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling , 2019 .

[53]  Leo Stroosnijder,et al.  Reducing Sediment Connectivity Through man‐Made and Natural Sediment Sinks in the Minizr Catchment, Northwest Ethiopia , 2017 .

[54]  B. Pradhan,et al.  Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. , 2019, Journal of environmental management.

[55]  B. Pradhan,et al.  Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm , 2018, Land Degradation & Development.

[56]  H. Pourghasemi,et al.  Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. , 2017, The Science of the total environment.

[57]  S. Keesstra,et al.  Soil-Related Sustainable Development Goals: Four Concepts to Make Land Degradation Neutrality and Restoration Work , 2018, Land.

[58]  Maria Ferentinou,et al.  Shallow landslide susceptibility assessment in a semiarid environment — A Quaternary catchment of KwaZulu-Natal, South Africa , 2016 .

[59]  Alireza Arabameri,et al.  Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence , 2019, Water.

[60]  Jesús Rodrigo-Comino,et al.  Assessment of the Sustainability of the Territories Affected by Gully Head Advancements through Aerial Photography and Modeling Estimations: A Case Study on Samal Watershed, Iran , 2018, Sustainability.

[61]  Gwo-Hshiung Tzeng,et al.  Extended VIKOR method in comparison with outranking methods , 2007, Eur. J. Oper. Res..

[62]  S. Pulley,et al.  Gully erosion as a mechanism for wetland formation: An examination of two contrasting landscapes , 2018 .

[63]  B. Pradhan,et al.  Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran , 2012 .

[64]  Mike Kirkby,et al.  Some factors controlling gully growth in fine-grained sediments: a model applied in southeast Spain. , 2000 .

[65]  E. Rotigliano,et al.  Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy) , 2014 .

[66]  Alireza Arabameri,et al.  Comparison and evaluation of three methods of multi attribute decision making methods in choosing the best plant species for environmental management (Case study: Chah Jam Erg) , 2015 .

[67]  M. Maerker,et al.  Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: A case study in the Mazayejan plain, southwest Iran , 2014 .

[68]  Fachao Qin,et al.  Characterizing the morphology of gully cross-sections based on PCA: a case of Yuanmou Dry-Hot Valley. , 2015 .

[69]  A. Kornejady,et al.  Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran , 2015 .

[70]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[71]  Mohammad Hossein Ramesht,et al.  Site Selection of Landfill with emphasis on Hydrogeomorphological – environmental parameters Shahrood-Bastam watershed , 2016 .

[72]  Manuel E. Mendoza,et al.  EFFECTS OF CONVERTING FOREST TO AVOCADO ORCHARDS ON TOPSOIL PROPERTIES IN THE TRANS‐MEXICAN VOLCANIC SYSTEM, MEXICO , 2014 .

[73]  R. Morgan,et al.  Threshold conditions for initiation of valley-side gullies in the Middle Veld of Swaziland , 2003 .

[74]  Fethi Bouksila,et al.  Combining field monitoring and aerial imagery to evaluate the role of gully erosion in a Mediterranean catchment (Tunisia) , 2018, CATENA.

[75]  Hamid Reza Pourghasemi,et al.  Identification of erosion-prone areas using different multi-criteria decision-making techniques and GIS , 2018 .

[76]  H. Pourghasemi,et al.  Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. , 2017, The Science of the total environment.

[77]  Lindsay C. Stringer,et al.  The onsite cost of gully erosion and cost‐benefit of gully rehabilitation: A case study in Ethiopia , 2012 .

[78]  S. Keesstra,et al.  How can statistical and artificial intelligence approaches predict piping erosion susceptibility? , 2019, The Science of the total environment.

[79]  Hamid Reza Pourghasemi,et al.  Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms , 2018, Applied Sciences.