Journals / Journal of Agricultural Sciences / 2026 / Volume: 31 Issue: 2

Comparison of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon's Entropy Models in Erosion Susceptibility Analysis in Bingöl (Türkiye) with GIS

Orhan İnik(BINGOL UNIVERSITY, FACULTY OF AGRICULTURE)0000-0003-1473-1392
Mustafa Utlu(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, FEN-EDEBİYAT FAKÜLTESİ, COĞRAFYA BÖLÜMÜ, COĞRAFYA PR.)0000-0002-7508-4478
Pages
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Publication Date
2025-03-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conducting analyses based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Soil erosion is one of the most important and critical processes occurring in Türkiye, as in all parts of the world.
📊
Key Findings & Data:In the study, erosion susceptibility analysis was carried out using 4 different methods Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE) that are effectively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion, and 19 conditioning factors based on these methods.
💡
Conclusion & Impact:Conducting analyses based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Erosion susceptibility · Logistic regression (LR) · Weight of Evidence (WoE)

Abstract

Soil erosion is one of the most important and critical processes occurring in Türkiye, as in all parts of the world. It is of great importance to understand the processes that occur as soil erosion continues. The aim of this study is to determine the erosion susceptibility occurring in the Çapakçur Stream basin, one of the important erosion areas of Türkiye. In the study, erosion susceptibility analysis was carried out using 4 different methods Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE) that are effectively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion, and 19 conditioning factors based on these methods. Analysis Results Model performances were evaluated using Receiver Operating Characteristic (ROC) and Area under the Curve (AUC) values based on a dataset consisting of 840 training (70%) and 360 testing (30%) points. According to result of the AUC values show that Logistic regression seems to perform well on both training (AUC= 94.7%) and validating datasets (AUC=93.5%). On the other hand, Weight of Evidence training (AUC= 93.5%) and testing datasets (AUC= 91.4%), Frequency Ratio training (AUC= 93.5%) and testing datasets (AUC=92.4%) of the Weight of Evidence result show that AUC and ROC values similar to Logistic Regression result, but slightly lower than Logistic Regression. Additionally, Shannon Entropy shows that it performs lower than other methods on both training (AUC= 55.7%) and testing datasets (AUC= 56.3%). Conducting analyses based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.

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Cite This Article

İnik, O., Utlu, M. (2025). Comparison of Logistic Regression, Frequency Ratio, Weight of Evidence and Shannon's Entropy Models in Erosion Susceptibility Analysis in Bingöl (Türkiye) with GIS. Journal of Agricultural Sciences, Volume: 31 Issue: 2. https://doi.org/10.15832/ankutbd.1535974

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References (115)

  1. Ait Neceur H, Abdo H G, Igmoullan B, Namous M, Alshehri F & A Albanai J (2024). Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS. Environmental Earth Sciences 83(3): 121 https://doi.org/10.1007/s12665-024-11424-5
  2. Akgün A (2007). Ayvalık ve yakın çevresinin erozyon ve heyelan duyarlılığının Coğrafi Bilgi Sistemleri tabanlı incelenmesi. Doktora Tezi, Dokuz Eylül Üni. Fen Bilimleri Ens. İzmir.
  3. Akıncı H, Özalp A Y & Kılıçer S T (2015). Assessment of landslide susceptibility in planned areas using geographic information systems and AHP method: Artvin Example. Journal of Natural Hazards and Environment 1(1-2): 40-53 (In Turkish)
  4. Akıncı H, Doğan S & Kılıçoğlu C (2017). Landslide susceptibility mapping of Canik (Samsun) district using bayesian probability and frequency ratio models. Selcuk University Journal of Engineering Science and Technology 5(3): 283- 299 https://doi.org/10.15317/scitech.2017.89
  5. Al-Hinai H & Abdalla R (2021). Mapping Coastal Flood Susceptible Areas Using Shannon’s Entropy Model: The Case of Muscat Governorate, Oman. ISPRS Int J Geo-Information 10: 252. https://doi.org/10.3390/ijgi10040252
  6. Amiri M, Pourghasemi H R, Ghanbarian G A & Afzali S F (2019). Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma 340: 55–69. https://doi.org/10.1016/j.geoderma.2018.12.042
  7. Anabalagan R (1992). Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32: 269–277. https://doi.org/10.1016/0013-7952(92)90053-2
  8. Anonymous (1998). Management of Agricultural and Pasture Lands. National Environmental Action Plan. DTP, Ankara. (In Turkish)
  9. Anonymous (1987). Türkiye General Soil Management Planning. Ministry of Agriculture, Forestry and Rural Affairs, General Directorate of Rural Services. Ankara. (In Turkish)
  10. Arabameri A, Chen W, Loche M, Zhao X, Li Y, Lombardo L & Bui DT (2020). Comparison of machine learning models for gully erosion susceptibility mapping. Geoscience Frontiers, 11(5): 1609-1620. https://doi.org/10.1016/j.gsf.2019.11.009
  11. Avcıoğlu A, Görüm T, Akbaş A, Moreno-de las Heras M, Yıldırım C & Yetemen Ö (2022). Regional distribution and characteristics of major badland landscapes in Turkey. Catena, 218, 106562. https://doi.org/10.1016/j.catena.2022.106562
  12. Bag R, Mondal I, Dehbozorgi M, Bank S P, Das D N, Bandyopadhyay J & Nguyen X C (2022). Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment. Journal of Cleaner Production 364: 132428.https://doi.org/10.1016/j.jclepro.2022.132428
  13. Baiddah A, Krimissa S, Hajji S, Ismaili M, Abdelrahman K & El Bouzekraoui M (2023). Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1184038
  14. Batista P V, Davies J, Silva M L & Quinton J N (2019). On the evaluation of soil erosion models: Are we doing enough?. Earth-Science Reviews 197: 102898. https://doi.org/10.1016/j.earscirev.2019.102898
  15. Beven K J & Kirkby M J (1979) A physically based, variable contributing area model ofbasin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J 24(1):43–69. https://doi.org/10.1080/02626667909491834
  16. Bhandari B P, Dhakal S & Tsou C Y (2024). Assessing the Prediction Accuracy of Frequency Ratio, Weight of Evidence, Shannon Entropy, and Information Value Methods for Landslide Susceptibility in the Siwalik Hills of Nepal. Sustainability, 16(5), 2092. https://doi.org/10.3390/su16052092
  17. Bonham Carter G F (1994). Geographic Information Systems for geoscientists, Modeling with GIS. Pergamon Press, Oxford.
  18. Bouamrane A, Boutaghane H, Bouamrane A, Dahri N, Abida H, Saber M, Kantoush S A & Sumi T (2024). Soil erosion susceptibility prediction using ensemble hybrid models with multicriteria decision-making analysis: Case study of the Medjerda basin, northern Africa. International Journal of Sediment Research. https://doi.org/10.1016/j.ijsrc.2024.08.003
  19. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V & Reichenbach P (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Processes and Landforms. 16(5): 427-445. https://doi.org/10.1002/esp.3290160505
  20. Çevik E & Topal T (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology 44 (8): 949-962 https://doi.org/10.1007/s00254-003-0838-6
  21. Chakrabortty R, Pal S C, Sahana M, Mondal A, Dou J, Pham B T & Yunus A P (2020). Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Natural hazards. https://doi.org/10.1007/s11069-020-04213-3
  22. Chalise D, Kumar L, Spalevic V & Skataric G (2019). Estimation of sediment yield and maximum outflow using the IntErO model in the sarada river basin of Nepal. Water 11: 952. https://doi.org/10.3390/w11050952.
  23. Chen W, Panahi M & Pourghasemi H R (2017). Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro- fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena 157: 310-324
  24. Chen W, Sun Z & Han J (2019). Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Applied sciences 9(1): 171. https://doi.org/10.3390/app9010171
  25. Choubin B, Rahmati O, Tahmasebipour N, Feizizadeh B & Pourghasemi H R (2019). Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. In Advances in Natural and Technological Hazards Research (Vol. 48, pp. 105–125). Springer Netherlands. https://doi.org/10.1007/978-3-319-73383-8_5
  26. Conforti M, Aucelli P P C, Robustelli G & Scarciglia F (2011). Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural Hazards 56(3): 881–898. https://doi.org/10.1007/s11069-010-9598-2
  27. Conoscenti C, Agnesi V, Angileri S, Cappadonia C, Rotigliano E & Märker M (2013). A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ Earth 70: 1179–1195. https://doi.org/10.1007/s12665-012-2205-y
  28. Conoscenti C, Di Maggio C & Rotigliano E (2008). Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily. Natural hazards 46: 287-305. https://doi.org/10.1007/s11069-007-9188-0
  29. Corsini A, Cervi F & Ronchetti F (2009). Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology, 111(1-2), 79- 87). doi: 10.1016/j.geomorph.2008.03.015
  30. Dai F C & Lee C F (2002). Landslide characteristics and slope instablity modelling using GIS, Lantau Island, Hong Kong. Geomorphology 42: 213-228
  31. Dai F C, Lee C F, Li J & Xu Z W (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmetal Geology 40(3): 381-391
  32. Danacıoğlu Ş & Tağıl Ş (2017). Assessment of Erosion Risk in Bakırçay Basin Using Rusle Model. Journal of Balıkesir University Social Sciences Institute 20(37) (in Turkish)
  33. Demir Y, Meral A & Doğan Demir A (2022). Estimation of Soil Losses in Çapakcur Watershed (Bingol, Turkey) Using RUSLE Method and Comparison of Predicted Soil Losses with Sediment Yield. Kahramanmaraş Sütçü İmam University Journal of Agriculture and Nature, 25(Supplement Issue 2) 523–537. https://doi.org/10.18016/ksutarimdoga.vi.1059631 (In Turkish)
  34. Dengiz O, İmamoğlu A, Saygın F, Göl C, Ediş S & Doğan A (2014). Soil erosion risk assessment of Inebolu basin with icona model. Anatolian Journal of Agricultural Sciences 29(2): 136-142. doi: 10.7161/anajas.2014.29.2.136-14 (In Turkish)
  35. Dengiz O, Öztaş T, Haliloğlu M & Şahin K (2020). Balancing of Land Degradation. Turkish Agricultural Engineering IX.Technical Congress. 81-104 (In Turkish)
  36. Ding Q, Chen W & Hong H (2017). Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto international, 32(6): 619-639. https://doi.org/10.1080/10106049.2016.1165294
  37. Doneus M (2013). Openness as visualization technique for interpretative mapping of airborne lidar derived digital terrain models. Remote Sens 5: 6427–6442. https://doi.org/10.3390/rs5126427
  38. Dramis F & Gentili B (1977). Contributo allo studio delle acclività dei versanti nell’appennino umbro-marchigiano. Studi Geologici Camerti 3: 153-164
  39. Du G L, Zhang Y S, Iqbal J, Yang Z H & Yao X (2017). Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science 14: 249-268. https://doi.org/10.1007/s11629-016-4126-9
  40. Duniway M C, Pfennigwerth A A, Fick S E, Nauman T W, Belnap J & Barger N N (2019). Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere, 10(3), e02650. https://doi.org/10.1002/ecs2.2650
  41. El Miloudi Y, El Kharim Y, Bounab A & El Hamdouni R (2024). Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco). Land, 13(2). https://doi.org/10.3390/land13020176
  42. Fick S E & Hijmans R J (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302– 4315. https://doi.org/10.1002/joc.5086
  43. Foumelis M, Lekkas E & Parcharidis I (2004). Landslide susceptibility mapping by GIS-based qualitative weighting procedure in Corinth area. Bulletin of the Geological Society of Greece XXXVI, 904– 912. In: Proceedings of the 10th international congress, Thes- saloniki, April 2004
  44. Garosi Y, Sheklabadi M, Pourghasemi H R, Besalatpour A A, Conoscenti C & Van Oost K (2018). Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma 330: 65–78. https://doi.org/10.1016/j.geoderma.2018.05.027
  45. Gayen A & Saha S (2017). Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India. Modeling Earth Systems and Environment, 3(3), 1123–1139. https://doi.org/10.1007/s40808-017-0362-4
  46. Graham M H (2003). Confronting Multicollinearity In Ecological Multiple Regression. Ecology 84:2809–2815. https://doi.org/10.1890/02- 3114
  47. Güney Y (2018). Use of frequency ratio method in erosion susceptibility analysis: Selendi Stream Basin (Manisa) example. Journal of Soil Science and Plant Nutrition 6(2): 73-85. (In Turkish)
  48. Guzzetti F, Carrara A, Cardinali M & Reichenbach P (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31 (1-4): 181-216. https://doi.org/10.1016/s0169-555x(99)00078-1
  49. Haan C T, Barfield B J & Hayes J C (1994). Design Hydrology and Sedimentology For Small Catchments. Academic Press An Imprint of Elseiver New York 38-101
  50. Habib M (2021). Quantifying topographic ruggedness using principal component analysis. Advances in Civil Engineering, 1-20. https://doi.org/10.1155/2021/3311912
  51. He S, Pan P, Dai L, Wang H & Liu J (2012). Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology, 171: 30-41. https://doi.org/10.1016/j.geomorph.2012.04.024
  52. Hembram T, Paul G C & Saha S (2019). Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: a comparison of information value and logistic regression models. Modeling Earth Systems and Environment 5(2): 689–708. https://doi.org/10.1007/s40808-018-0560-8
  53. Hembram T K, Paul G C & Saha S (2020). Modelling of gully erosion risk using new ensemble of conditional probability and index of entropy in Jainti River basin of Chotanagpur Plateau Fringe Area, India. Appl Geomatics 12:337–360. https://doi.org/10.1007/s12518-020-00301-y
  54. İDEP (2012). Climate Change National Action Plan 2011-2023, Ministry of Environment and Urbanization, Ankara. (In Turkish)
  55. İnik O (2022). Negative Effects of Land Degradation on Human Life. “Innovative Methods, Theories and Applications in Health Sciences”. Iksad Publications.ISBN: 978-625-8213-40-9. Ankara. (In Turkish)
  56. İnik O (2023). Investigation of Land Degradation Balancing Studies in Çapakçur Microcatchment of Bingöl Province. Atatürk University, Institute of Science. (Doctoral Thesis) (In Turkish)
  57. İnik O, İnik Ö, Öztaş T & Yüksel A (2022). Soil Temperature Prediction with Long Short Term Memory (LSTM). Turkish Journal of Agriculture and Natural Sciences, 9(3): 779-785. https://doi.org/10.30910/turkjans.1101753
  58. Islam S, Tahir M & Parveen S (2022) GIS-based flood susceptibility mapping of the lower Bagmati basin in Bihar, using Shannon’s entropy model. Model Earth Syst Environ 8:3005–3019. https://doi.org/10.1007/s40808-021-01283-5
  59. Jaafari A, Najafi A, Pourghasemi H R, Rezaeian J & Sattarian A (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology 11: 909- 926. https://doi.org/10.1007/s13762-013-0464-0
  60. Jasiewicz J & Stepinski T F (2013). Geomorphology 182: 147-156. ScienceDirect
  61. Jenks G (1967). The Data Model Concept in Statistical Mapping. In International Yearbook of Cartography (pp. 7:186-190)
  62. Jenks G F (1967). The data model concept in statistical mapping. International Yearbook of Cartography 7: 186–190
  63. Kakembo V, Xanga W W & Rowntree K (2009). Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa. Geomorphology 110:188–194. https://doi.org/10.1016/j.geomorph.2009.04.006
  64. Karagöz A, Doğan O, Erpul G, Dengiz O, Sönmez B, Tekeli İ, Saygın S D & Madenoğlu S (2015). Evaluation of the Possible Effects of Desertification, Drought and Erosion in Turkey. Proceedings Book of the 8th Turkish Agricultural Engineering Technical Congress-1, 118. (In Turkish)
  65. Kelava A, Moosbrugger H, Dimitruk P & Schermelleh-Engel K (2008). Multicollinearity and Missing Constraints. Methodology 4:51–66. https://doi.org/10.1027/1614-2241.4.2.51
  66. Kheir R B, Abdallah C & Khawlie M (2008). Assessing soil erosion in Mediterranean karst landscapes of Lebanon using remote sensing and GIS. Engineering Geology, 99(3-4): 239-254. https://doi.org/10.1016/j.enggeo.2007.11.012
  67. Kılıçoğlu C (2020). Production of landslide susceptibility map of Vezirköprü district of Samsun province using frequency ratio method and bayesian probability model. Afyon Kocatepe University Journal of Science and Engineering Sciences, 20(1), 138-154 (In Turkish)
  68. Kleinbaum D G, Dietz K, Gail M, Klein M & Klein M (2002). Logistic regression (p. 536). New York: Springer-Verlag.
  69. Lana J C, Castro P D T A & Lana C E (2022). Assessing gully erosion susceptibility and its conditioning factors in southeastern Brazil using machine learning algorithms and bivariate statistical methods: A regional approach. Geomorphology, 402, 108159. https://doi.org/10.1016/j.geomorph.2022.108159
  70. Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A & Mosavi A (2020). GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sensing, 12(15): 2478. https://doi.org/10.3390/rs12152478
  71. Lin J (1991). Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory 37:145–151. https://doi.org/10.1109/18.61115
  72. Maharaj R J (1993). Landslide processes and landslide susceptibility analysis from an Upland Watershed: a case study from St Andrew, Jamaica, West Indies. Engineening Geology 34 (1-2): 53-79. https://doi.org/10.1016/0013-7952(93)90043-c
  73. Miao F, Zhao F, Wu Y, Li L & Török Á (2023). Landslide susceptibility mapping in Three Gorges Reservoir area based on GIS and boosting decision tree model. Stochastic Environmental Research and Risk Assessment, 37(6): 2283-2303. https://doi.org/10.1007/s00477-023- 02394-4
  74. Mohammed S, Al-Ebraheem A, Holb I J, Alsafadi K, Dikkeh M, Pham Q B, Linh N T T & Szabo S (2020). Soil management effects on soil water erosion and runoff in Central Syria—a comparative evaluation of general linear model and random forest regression. Water 12, 2529. https://doi.org/10.3390/w12092529.
  75. Moore I & Burch G (1986). Physical Basis of the Length – slope Factor in the Universal Soil Loss Equation. Soil Science Society of America Journal 50: 1294-1298
  76. Morgan R P C & Nearing M (2016). Handbook of erosion modelling. John Wiley & Sons.
  77. Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A & Khıre M V (2000). Landslide hazard suspectibility mapping based on terrain and climatic factors for tropical monsoon regions. Bulletin Engineering Geology and the Environment 58(4): 275-287. https://doi.org/10.1007/s100649900032
  78. Neuhauser B & Terhorst B (2007). Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86(1-2): 12-24. https://doi.org/10.1016/j.geomorph.2006.08.002
  79. Nikolova V, Mitova M & Dimitrov E (2022). Topographic factor of water erosion–analysis of watershed morphometry and RUSLE LS factor in GIS environment. Review of the Bulgarian Geological Society 83(1): 3-14. https://doi.org/10.52215/rev.bgs.2022.83.1.3
  80. Nkonge L K, Gathenya J M, Kiptala J K, Cheruiyot C K & Petroselli A (2023). An Ensemble of Weight of Evidence and Logistic Regression for Gully Erosion Susceptibility Mapping in the Kakia-Esamburmbur Catchment, Kenya. Water (Switzerland), 15(7). https://doi.org/10.3390/w15071292
  81. Nkonge L K, Gathenya J M, Kiptala J K, Cheruiyot C K & Petroselli A (2023). An Ensemble of Weight of Evidence and Logistic Regression for Gully Erosion Susceptibility Mapping in the Kakia-Esamburmbur Catchment, Kenya. Water (Switzerland) 15(7): https://doi.org/10.3390/w15071292
  82. Ogbonna J U, Alozie M, Nkemdırım V & Eze M U (2011). GIS analysis for mapping gully erosion impacts on the geo-formation of the Old Imo State, Nigeria. ABSU Journal of Environment Science and Technology 1: 48-61
  83. Ohlmacher G C (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91:117–134. https://doi.org/10.1016/j.enggeo.2007.01.005
  84. Öztürk D (2017). Investigation of Urban Sprawl with Shannon Entropy and Fractal Analysis: Samsun Example. 16th Turkey Scientific and Technical Congress on Mapping pp. 3-6. (In Turkish)
  85. Öztürk M Z, Çetinkaya G & Aydın S (2017). Climate Types of Turkey According to Köppen-Geiger Climate Classification. Journal of Geography, 35: 17–27. https://doi.org/10.26650/jgeog295515
  86. Peel M C, Finlayson B L & McMahon T A (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences 11(5): 1633–1644. https://doi.org/10.5194/hess-11-1633-2007
  87. Podhrazska J, Kučera J, Karasek P & Konečná J (2015). Land Degradation by Erosion and Its Economic Consequences for the Region of South Moravia (Czech Republic). Soil & Water Research, 10(2). https://doi.org/10.17221/143/2014-swr
  88. Pulice I, Scarcıglia, F, Leonardi L, Robustelli G, Conforti M, Cuscıno M, Lupiano V & Critelli S (2009). Studio multidisciplinare di forme e processi denudazionali Nell’area di Vrica (Calabria Orientale). Bollettino della Società Geografica Italiana. 87(I–II): 399-414.
  89. Rahmati O, Haghizadeh A, Pourghasemi H R & Noormohamadi F (2016). Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Natural Hazards 82(2): 1231–1258. https://doi.org/10.1007/s11069-016-2239-7
  90. Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi H R & Feizizadeh B (2017). Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology 298: 118–137. https://doi.org/10.1016/j.geomorph.2017.09.006
  91. Regmi N R, Giardino J R & Vitek J D (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1-2): 172-187. https://doi.org/10.1016/ geomorph.2009.10.002
  92. Roy J & Saha DS (2019). GIS-based Gully Erosion Susceptibility Evaluation Using Frequency Ratio, Cosine Amplitude and Logistic. Regression Ensembled with fuzzy logic in Hinglo River Basin, India. Remote Sens Appl Soc Environ 15:100247. https://doi.org/10.1016/j.rsase.2019.100247
  93. Różycka M, Migoń P & Michniewicz A (2017). Topographic Wetness Index and Terrain Ruggedness Index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Zeitschrift für geomorphologie, Supplementary issues 61(2): 61-80. https://doi.org/10.1127/zfg_suppl/2016/0328
  94. Saxena S (2021). A study on causes and consequences of soil erosion. Asian Journal of Research in Business Economics and Management, 11(10): 100-105. https://doi.org/10.5958/2249-7307.2021.00036.0
  95. Saygın S D (2013). Climate Change and Global Warming: What Awaits Us? Köy-Koop News, April, 2013 Page: 17. (In Turkish)
  96. Sharma B & Pandey A (2022). Mapping of Erosion Hazard in and around Kharagpur Hills, Bihar using hydrological indices. In MOL2NET’22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering. Basel, Switzerland: MDPI. https://doi.org/10.3390/mol2net-08-12638
  97. Sharma L P, Patel N, Ghose M K & Debnath P (2012). Influence of shannon’s entropy on landslide-causing parameters for vulnerability study and zonation-a case study in sikkim, india. Arab J Geosci 5: 421–431. https://doi.org/10.1007/s12517-010-0205-3
  98. Stepinski T F & Jasiewicz J (2011). Geomorphometry Papers, Redlands pp. 109-112 Sterk G (2003). Causes, consequences and control of wind erosion in Sahelian Africa: a review. Land Degradation & Development, 14(1): 95- 108. https://doi.org/10.1002/ldr.526
  99. Tehrany M S, Pradhan B & Jebur M N (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504: 69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034
  100. Trevisani S, Teza G & Guth P L (2023). Hacking the topographic ruggedness index. Geomorphology, 439: 108838. https://doi.org/10.1016/j.geomorph.2023.108838
  101. UNCCD (2016). Science-Policy Notes. Balanced Lands-Balancing Land Degradation Scientific Concept Framework. 02 September 2016.).
  102. Utlu M (2023). Flood Susceptibility Analysis of Ezine Stream Basin (Kastamonu-Bozkurt) Using Frequency Ratio and Shannon Entropy Method. Journal of Geomorphological Research pp. 160–178. https://doi.org/10.46453/jader.1358845 (In Turkish)
  103. Valentin C, Poesen J & Li Y (2005). Gully erosion: Impacts, factors and control. Catena 63: 132–153. https://doi.org/10.1016/j.catena.2005.06.001
  104. Van Westen C J, Rengers N & Soeters R (2003). Use of geomorphological information in indirect landslide susceptibility assessment, Natural Hazards 30(2003): 399-419. https://doi.org/10.1023/b:nhaz.0000007097.42735.9e
  105. Wang F, Sahana M, Pahlevanzadeh B, Pal S C, Shit P K, Piran M J & Mosavi A (2021). Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility. Alexandria Engineering Journal 60(6): 5813-5829. https://doi.org/10.1016/j.aej.2021.04.026
  106. Xu C, Xu X, Lee Y H, Tan X, Yu G & Dai F (2012). The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling. Environmental Earth Sciences 66: 1603-1616. https://doi.org/10.1007/s12665-012-1624-0
  107. Yufeng S & Fengxiang J (2009). Landslide Stability Analysis Based on Generalized Information Entropy. 2009 International Conference on Environmental Science and Information Application Technology. IEEE, pp. 83–85
  108. Yulianto F, Fitriana H L & Sukowati K A D (2020). Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia. Model Earth Syst Environ 6:383–395. https://doi.org/10.1007/s40808-019-00686-9
  109. Zabihi M, Mirchooli F, Motevalli A, Darvishan A K, Pourghasemi H R, Zakeri M A & Sadighi F (2018). Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena, 161: 1-13. https://doi.org/10.1016/j.catena.2017.10.010
  110. Zabihi M, Pourghasemi H R, Motevalli A & Zakeri M A (2019). Gully erosion modeling using GIS-based data mining techniques in Northern Iran: a comparison between boosted regression tree and multivariate adaptive regression spline. Advances in Natural and Technological Hazards Research, 1-26. https://doi.org/10.1007/978-3-319-73383-8_1
  111. Zhang Z, Xu W, Li L, Huang J, Deng, L & Wang Q (2021). Effects of temporal conservation measures on water erosion processes of disturbed soil accumulation in construction projects. Journal of Cleaner Production, 319, 128612. https://doi.org/10.1016/j.jclepro.2021.128612
  112. Zhao J, Wang Z, Dong Y, Yang Z & Govers G (2022). How soil erosion and runoff are related to land use, topography and annual precipitation: Insights from a meta-analysis of erosion plots in China. Science of The Total Environment, 802, 149665. https://doi.org/10.1016/j.scitotenv.2021.149665
  113. Zhu H, Tang G, Qian K & Liu H (2014). Extraction and analysis of gully head of Loess Plateau in China based on digital elevation model. Chinese geographical science 24: 328-338. https://doi.org/10.1007/s11769-014-0663-8
  114. Zhuang Y, Du C, Zhang L, Du Y & Li S (2015). Research trends and hotspots in soil erosion from 1932 to 2013: A literature review. Scientometrics 10: 743–758. doi: 10.1007/s11192-015-1706-3
  115. İnik O, İnik Ö, Öztaş T & Yüksel A (2022). Soil Temperature Prediction with Long Short Term Memory (LSTM). Turkish Journal of Agriculture and Natural Sciences, 9(3): 779-785. https://doi.org/10.30910/turkjans.1101753