[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124460-en":3,"doc-seo-124460-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124460,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Modeling Soil Erosion Susceptibility Using Machine Learning Techniques - Rud-e-Faryab Basin, Iran","Soil erosion threatens water and soil resources, undermining agricultural productivity, infrastructure integrity, and environmental stability. This study models erosion susceptibility in the Rud-e-Faryab basin in Iran by applying the BIOMOD-2 R package, integrating an ensemble of 10 machine learning algorithms across 10 key environmental variables. Erosion-event field data support model training and validation, with performance assessed using ROC, KAPPA, and TSS. GLM, RF, ANN, SRE, and MARS yield varying accuracies by erosion type, while geological formation, slope, and soil resources emerge as major drivers.","Land Degradation & Development  \nRESEARCH ARTICLE  OPEN ACCESS   \nModeling Soil Erosion Susceptibility Using Machine Learning Techniques: Rud-e-Faryab Basin, Iran  \nJavad Momeni Damaneh1  | Ali Akbar Safdari2 | Nazanin Azarnejad3,4  | Majid Ghorbani3,4  | Fatemeh Panahi5  | Sayed Fakhreddin Afzali6  | Stefano Loppi3,7   \n1Department of Natural Resources Engineering, Agriculture and Natural Resources Faculty, Hormozgan University, Bandar Abbas, Iran | 2Department of Natural Resources Engineering, Natural Resources Faculty, Tehran University, Tehran, Iran | 3Department of Life Sciences, University of Siena, Siena, Italy | 4School of Environmental and Natural Sciences, Bangor University, Bangor, UK | 5Department of Desert Sciences Engineering, Faculty of Natural Resources Earth Sciences, University of Kashan, Kashan, Iran | 6Department of Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran | 7BAT Center – Interuniversity Center for Studies on Bioinspired Agro-Environmental Technology, University of Naples “Federico II”, Napoli, Italy  \nCorrespondence: Majid Ghorbani ([m.ghorbani@student.unisi.it](m.ghorbani@student.unisi.it))  \nReceived: 15 January 2025 | Revised: 29 June 2025 | Accepted: 7 July 2025  \nFunding: The authors received no specific funding for this work.  \nKeywords: BIOMOD-2 | erosion forms | erosion mapping | machine learning | potential erosion | soil protection  \nABSTRACT  \nSoil erosion poses a significant threat to water and soil resources, affecting agricultural productivity, infrastructure, and environmental stability. This study models erosion susceptibility in the Rud-e-Faryab basin (Bushehr province, Iran) using the BIOMOD-2 package in R (an ensemble of 10 machine learning algorithms) applied to 10 important environmental variables. Field data on erosion events were used to train and validate the model, and the performance of the model was evaluated using ROC, KAPPA, and TSS coefficients. The results indicate different accuracies for different erosion types, highlighting the GLM, RF, ANN, SRE, and MARS models. Geological formation, slope, and soil resources were found to be the most important factors for erosion susceptibility in the study. Key innovations of this study include (1) the first-time adaptation of the BIOMOD-2 package for soil erosion assessment,(2) the introduction of a stability analysis framework with 10 repeated model runs to test reproducibility, and (3) a comprehensive comparison of 10 machine learning models to identify context-specific optimal approaches. These contributions provide a robust, replicable framework for erosion risk mapping that is particularly valuable in regions with sparse data, and provide actionable insights for sustainable land use planning and resource management.  \n1 | Introduction  \nSoil erosion stands as a primary constraint to the optimal and sustainable utilization of water and soil resources (Kulimushi et al. 2023) . Therefore, understanding the erosion status of a region is crucial for comprehending watershed and management systems and making informed decisions (Abuzaid et al. 2023) . Sediment production, a significant consequence of soil erosion, yields both extra- and intra-regional effects in various forms (Williams 1983). Soil erosion directly and indirectly reduces the  \nproductive capacity of the soil in the affected areas and gradually affects the quantity and quality of the soil, so cropland and crop production will decrease each year because of increased erosion. In addition, all intra-and extra-regional impacts of soil erosion have direct or indirect economic consequences and negatively affect the country's economy, development programs, and sustainable development (Jafari et al. 2022) .  \nIn recent decades, modern societies have witnessed a notable increase in various forms of natural resource destruction,  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, dist","cbCaim1i9WKgCRrW","https://ap.wps.com/l/cbCaim1i9WKgCRrW","pdf",7783307,1,14,"English","en",105,"# Introduction\n## Soil erosion as a constraint\n## Land degradation and erosion impacts\n## Climate and regional susceptibility\n## River erosion and prioritizing conservation","[{\"question\":\"What method and data are used to model soil erosion susceptibility?\",\"answer\":\"The study uses the BIOMOD-2 package in R with an ensemble of 10 machine learning algorithms, trained and validated using field data from erosion events and 10 environmental variables.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is evaluated using ROC, KAPPA, and TSS coefficients.\"},{\"question\":\"Which factors most influence erosion susceptibility in the study area?\",\"answer\":\"Geological formation, slope, and soil resources are identified as the most important factors for erosion susceptibility.\"}]","Modeling Soil Erosion Susceptibility Using Machine Learning Techniques - Rud-e-Faryab Basin, Iran | PDF",1785822429,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"modeling-soil-erosion-susceptibility-using-machine-learning-techniques-rud-e-faryab-basin-iran","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/modeling-soil-erosion-susceptibility-using-machine-learning-techniques-rud-e-faryab-basin-iran/124460/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What method and data are used to model soil erosion susceptibility?","Question",{"text":75,"@type":76},"The study uses the BIOMOD-2 package in R with an ensemble of 10 machine learning algorithms, trained and validated using field data from erosion events and 10 environmental variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is model performance evaluated?",{"text":80,"@type":76},"Performance is evaluated using ROC, KAPPA, and TSS coefficients.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors most influence erosion susceptibility in the study area?",{"text":84,"@type":76},"Geological formation, slope, and soil resources are identified as the most important factors for erosion susceptibility.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]