[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123471-en":3,"doc-seo-123471-105":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},123471,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Soil erosion analysis based on machine learning method - Prediction and feature engineering using XGBoost","Soil erosion analysis through machine learning integrates remote sensing signals, climate indicators, and soil characteristics to predict and interpret erosion processes across varied landscapes. Spectral indices including NDVI, moisture stress index (MSI), and surface albedo are used to quantify vegetation condition, moisture status, and surface reflectance. An XGBoost-based framework performs erosion-stage classification with up to 99% accuracy, supported by systematic feature engineering, dataset preprocessing, and rigorous model evaluation. Comparative experiments against USLE and RUSLE show improved predictive performance for sensor monitoring and decision-support.","Soil erosion analysis based on machine learning method  \nMukhammed Bolsynbek1, Gulzira Abdikerimova1, Sandugash Serikbayeva1, Ardak Batyrkhanov2, Dana Shrymbay3, Zhazira Taszhurekova3, Gulkiz Zhidekulova4, Gulmira Shraimanova5  \n1Department of Information Systems, Faculty of Information Technology, L.N.Gumilyov Eurasian National University, Astana,  \nRepublic of Kazakhstan  \n2Department of Software Engineering, Faculty of Physics, Mathematics and Information Technology, Kh. Dosmukhamedov Atyrau  \nUniversity, Atyrau, Republic of Kazakhstan  \n3Department of Applied Informatics and Programming, Faculty of Technology, Taraz University named after M.Kh.Dulaty, Taraz,  \nRepublic of Kazakhstan  \n4Department of Information Systems, Faculty of Technology, Taraz University named after M.Kh.Dulaty, Taraz, Republic of  \nKazakhstan  \n5Department of Psychology, Pedagogy and Social Work, Faculty of Finance, Logistics and Digital Technologies, Karaganda University  \nofKazpotrebsoyuz, Karaganda, Republic of Kazakhstan  \nArticle history:  \nReceived Apr 10, 2025 Revised Sep 30, 2025 Accepted Oct 14, 2025  \nKeywords:  \nMachine learning Remote sensing Soil erosion Spectral indices XGBoost algorithm  \nCorresponding Author:  \nSoil erosion poses a serious environmental and agricultural threat that undermines land productivity, sustainability, and ecosystem stability. This study develops a robust machine learning framework for predicting and analyzing soil erosion across diverse landscapes by integrating advanced remote sensing data, climate indicators, and soil characteristics. Spectral indices such as the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo were employed to assess vegetation condition, moisture levels, and surface reflectance. The proposed model, based on the extreme gradient boosting (XGBoost) algorithm, classifies erosion stages with up to 99% accuracy, ranging from healthy land to severely degraded areas. The methodology includes comprehensive feature engineering, dataset preprocessing, and model evaluation. Furthermore, a comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the proposed approach. The findings offer valuable insights for sensor-based monitoring systems and cloud-based decision-support tools, supporting sustainable land use management, erosion risk mitigation, and effective soil conservation strategies.  \nThis is an open access article under the CC BY-SA license.  \nSandugash Serikbayeva  \nDepartment of Information Systems, Faculty of Information Technology L.N.Gumilyov Eurasian National University  \n010000 Astana, Republic of Kazakhstan  \nEmail: [Inf_8585@mail.ru](Inf_8585@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSoil erosion is one of the most pressing environmental issues today. It significantly affects agriculture, ecosystems, and global food security [1]–[3] . The main consequence of erosion is the loss of productive soil layers, which reduces the soil’s ability to retain water and harms its structure. This ultimately leads to decreased agricultural productivity and sustainability [4]–[6] . The main erosion processes—water, wind, and human activity—vary in intensity based on local environmental factors and human actions, creating different management challenges [7] . To effectively combat soil erosion, we need timely and accurate monitoring and forecasting. However, standard methods often fall short in providing complete  \ninformation. The rise of artificial intelligence and remote sensing in recent years has opened up new ways to monitor and predict soil erosion [8]–[10] .  \nHigh-resolution satellite data, particularly from Sentinel-2, has proven very useful. It allows for large-scale assessments of soil condition using spectral indices like the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo [11] . These parameters offer essential information on veget","cbCaicTgdo3JBHQ8","https://ap.wps.com/l/cbCaicTgdo3JBHQ8","pdf",818899,1,15,"English","en",105,"# 1. INTRODUCTION\n## Remote sensing and spectral indices\n## Machine learning and deep learning background\n# 2. STUDY OBJECTIVE","[{\"question\":\"What data sources and indicators are used to analyze soil erosion in this study?\",\"answer\":\"The study integrates advanced remote sensing data with climate indicators and soil characteristics. It uses spectral indices such as NDVI, MSI, and surface albedo to capture vegetation health, moisture levels, and surface reflectance.\"},{\"question\":\"Which machine learning method performs the main erosion-stage classification?\",\"answer\":\"The proposed model is based on extreme gradient boosting (XGBoost). It classifies erosion stages from healthy land to severely degraded areas with up to 99% accuracy.\"},{\"question\":\"How does the proposed approach compare with traditional erosion models?\",\"answer\":\"A comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the XGBoost-based approach for erosion risk assessment.\"}]","Soil erosion analysis based on machine learning method - Prediction and feature engineering using XGBoost | PDF",1785816705,38,{"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},"soil-erosion-analysis-based-on-machine-learning-method-prediction-and-feature-engineering-using-xgboost","",{"@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/soil-erosion-analysis-based-on-machine-learning-method-prediction-and-feature-engineering-using-xgboost/123471/",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 data sources and indicators are used to analyze soil erosion in this study?","Question",{"text":75,"@type":76},"The study integrates advanced remote sensing data with climate indicators and soil characteristics. It uses spectral indices such as NDVI, MSI, and surface albedo to capture vegetation health, moisture levels, and surface reflectance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning method performs the main erosion-stage classification?",{"text":80,"@type":76},"The proposed model is based on extreme gradient boosting (XGBoost). It classifies erosion stages from healthy land to severely degraded areas with up to 99% accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach compare with traditional erosion models?",{"text":84,"@type":76},"A comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the XGBoost-based approach for erosion risk assessment.","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"]