{"id":39768,"date":"2025-06-27T11:00:22","date_gmt":"2025-06-27T09:00:22","guid":{"rendered":"https:\/\/resoilfoundation.org\/?p=39768"},"modified":"2025-06-23T18:50:50","modified_gmt":"2025-06-23T16:50:50","slug":"ai-better-erosion-prediction","status":"publish","type":"post","link":"https:\/\/resoilfoundation.org\/en\/innovation-technology\/ai-better-erosion-prediction\/","title":{"rendered":"How AI will ensure better prediction of erosion"},"content":{"rendered":"<h2>The University of Illinois, tested a machine learning system that combines several predictive models of erosion. With better results than individual algorithms<\/h2>\n<p style=\"text-align: center;\">by Matteo Cavallito<\/p>\n<p>&nbsp;<\/p>\n<p>Erosion represents one of the <a href=\"https:\/\/resoilfoundation.org\/en\/environment\/soil-erosion-usa-speed\/\" target=\"_blank\" rel=\"noopener\">most worrisome<\/a> phenomena for the Planet&#8217;s soils. But not all such events are created equal. Indeed, among the various forms of degradation, <strong>gully erosion<\/strong> emerget as the most intense form. This phenomenon, caused by heavy rainfall, produces deep channels &#8211; called <a href=\"https:\/\/en.wikipedia.org\/wiki\/Badlands\" target=\"_blank\" rel=\"noopener\">badlands<\/a> &#8211; in the countryside, compromising soil fertility and increasing sediment transport into waterways.<\/p>\n<p>In this scenario, of course,<strong> identifying in advance the areas at risk<\/strong> allows for targeted interventions, such as land cover, drainage and vegetation management. Essential, in short, is predictive capacity, a resource that today, one study argues, can be further improved. Thanks to <strong>artificial intelligence<\/strong>.<\/p>\n<div class='avia-iframe-wrap'>\n<blockquote class=\"wp-embedded-content\" data-secret=\"QPiIgfCcnt\"><p><a href=\"https:\/\/resoilfoundation.org\/en\/articles\/climate-erosion-california\/\">In California, fires and subsequent floods accelerate erosion<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;In California, fires and subsequent floods accelerate erosion&#8221; &#8212; Re Soil Foundation\" src=\"https:\/\/resoilfoundation.org\/en\/articles\/climate-erosion-california\/embed\/#?secret=RnBPODFm6x#?secret=QPiIgfCcnt\" data-secret=\"QPiIgfCcnt\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/div>\n<h5>The study<\/h5>\n<p>Published in the Journal of Environmental Management, the <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0301479725014549?via%3Dihub#ack0010\" target=\"_blank\" rel=\"noopener\">research<\/a>, by a team of scientists from the <strong>University of Illinois Urbana-Champaign<\/strong>, proposed a new predictive methodology that was tested in a rural area of Jefferson County in the southern part of the state. The area, the study explains, \u201cfeatures rolling topography and is about 60% agricultural land, primarily used for <strong>growing corn and soybeans<\/strong>.\u201d<\/p>\n<blockquote><p>The machine learning model tested is based on the <a href=\"https:\/\/en.wikipedia.org\/wiki\/Ensemble_learning\" target=\"_blank\" rel=\"noopener\">stacking ensemble<\/a> technique. That is, on the<strong> combination of several predictive algorithms<\/strong>.<\/p><\/blockquote>\n<p>Such a system is able to exploit the strengths of each model to improve the overall accuracy. On the occasion, the researchers mapped \u201cthe study area based <strong>on elevation differences<\/strong> observed between 2012 and 2015.\u201d In training and validating the models, moreover, the authors examined<strong> 25 variables<\/strong> \u201cthat can affect erosion susceptibility, including topography, soil properties, vegetation features, and precipitation patterns.\u201d The results were promising.<\/p>\n<div class='avia-iframe-wrap'>\n<blockquote class=\"wp-embedded-content\" data-secret=\"XDEQPpGJSl\"><p><a href=\"https:\/\/resoilfoundation.org\/en\/articles\/permafrost-erosion-co2-oceans\/\">Permafrost erosion reduces carbon sequestration in the oceans<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Permafrost erosion reduces carbon sequestration in the oceans&#8221; &#8212; Re Soil Foundation\" src=\"https:\/\/resoilfoundation.org\/en\/articles\/permafrost-erosion-co2-oceans\/embed\/#?secret=Ecw234WTy5#?secret=XDEQPpGJSl\" data-secret=\"XDEQPpGJSl\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/div>\n<h5>More accurate information on erosion<\/h5>\n<p>A total of 44 different models were evaluated each consisting of a combination of basic models. Then, says an <a href=\"https:\/\/aces.illinois.edu\/news\/illinois-study-novel-ai-methodology-improves-gully-erosion-prediction-and-interpretation\" target=\"_blank\" rel=\"noopener\">article<\/a> published by the University of Illinois, the authors \u201ccreated<strong> gully erosion susceptibility maps<\/strong> using the best-performing stacking model and four individual models.\u201d The performance was significantly better than that achieved by the individual algorithms.<\/p>\n<blockquote><p>In fact, the researchers \u201cfound that the best stacking model achieved a <strong>prediction accuracy of 91.6 %<\/strong>, compared to 86% for the best individual model.\u201d<\/p><\/blockquote>\n<p>With the use of a specific tool known as <strong>SHAP (SHapley Additive exPlanations)<\/strong>, an artificial intelligence-based technique that assigns each variable a specific impact, it was also possible to identify the weight of the individual factors involved. The system, in particular, \u201cidentified the annual <strong>leaf area index<\/strong> (LAI) as the most influential feature in both base models.\u201d Simplifying, the article continues, it means that \u201cgreater leaf coverage <strong>reduces the direct impact of rainfall on soil,<\/strong> which in turn decreases the severity of erosion.\u201d<\/p>\n<div class='avia-iframe-wrap'>\n<blockquote class=\"wp-embedded-content\" data-secret=\"9tHIEty3Dh\"><p><a href=\"https:\/\/resoilfoundation.org\/en\/articles\/china-black-soil-campaign\/\">China launches anti-erosion black soil campaign<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;China launches anti-erosion black soil campaign&#8221; &#8212; Re Soil Foundation\" src=\"https:\/\/resoilfoundation.org\/en\/articles\/china-black-soil-campaign\/embed\/#?secret=bEr9exu742#?secret=9tHIEty3Dh\" data-secret=\"9tHIEty3Dh\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/div>\n<h5>A resource for informed decisions<\/h5>\n<p>According to scientists, the research provides a useful tool for <strong>land-use planning and hydrological risk management<\/strong>. Indeed, by creating more accurate risk maps, artificial intelligence helps effectively guide targeted prevention interventions, such as reforestation, runoff management or infrastructure protection. The research, in short, introduces \u201can innovative, explainable stacking ensemble model <strong>to enhance predictive performance<\/strong> for gully erosion susceptibility prediction.\u201d Thus providing \u201ctransparent interpretations of model decisions,\u201d the authors conclude.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>L&#8217;Universit\u00e0 dell\u2019Illinois, ha testato un sistema di apprendimento automatico che combina diversi modelli predittivi dell&#8217;erosione. Con risultati migliori rispetto a quelli ottenuti dai singoli algoritmi<\/p>\n","protected":false},"author":4,"featured_media":39669,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[152,134],"tags":[318,321,247,188,322,163],"class_list":["post-39768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation-technology","category-articles","tag-dissesto-idrogeologico-en","tag-erosione-en","tag-monitoraggio-en","tag-research","tag-suolo-en","tag-usa-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How AI will ensure better prediction of erosion<\/title>\n<meta name=\"description\" content=\"Creando mappe di rischio erosione pi\u00f9 precise, l\u2019intelligenza artificiale aiuta a guidare in modo efficace interventi di prevenzione\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/resoilfoundation.org\/en\/innovation-technology\/ai-better-erosion-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How AI will ensure better prediction of erosion\" \/>\n<meta property=\"og:description\" content=\"Creando mappe di rischio erosione pi\u00f9 precise, l\u2019intelligenza artificiale aiuta a guidare in modo efficace interventi di prevenzione\" \/>\n<meta property=\"og:url\" content=\"https:\/\/resoilfoundation.org\/en\/innovation-technology\/ai-better-erosion-prediction\/\" \/>\n<meta property=\"og:site_name\" content=\"Re Soil Foundation\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/resoilfoundation\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-06-27T09:00:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/resoilfoundation.org\/wp-content\/uploads\/2025\/06\/erosione.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Matteo Cavallito\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@re_soil\" \/>\n<meta name=\"twitter:site\" content=\"@re_soil\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Matteo Cavallito\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/resoilfoundation.org\\\/en\\\/innovation-technology\\\/ai-better-erosion-prediction\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/resoilfoundation.org\\\/en\\\/innovation-technology\\\/ai-better-erosion-prediction\\\/\"},\"author\":{\"name\":\"Matteo Cavallito\",\"@id\":\"https:\\\/\\\/resoilfoundation.org\\\/en\\\/#\\\/schema\\\/person\\\/9deaff5320c99714a3a7944e4b7c8fbe\"},\"headline\":\"How AI will ensure better prediction of erosion\",\"datePublished\":\"2025-06-27T09:00:22+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/resoilfoundation.org\\\/en\\\/innovation-technology\\\/ai-better-erosion-prediction\\\/\"},\"wordCount\":543,\"image\":{\"@id\":\"https:\\\/\\\/resoilfoundation.org\\\/en\\\/innovation-technology\\\/ai-better-erosion-prediction\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/resoilfoundation.org\\\/wp-content\\\/uploads\\\/2025\\\/06\\\/erosione.jpg\",\"keywords\":[\"dissesto idrogeologico\",\"erosione\",\"monitoraggio\",\"research\",\"suolo\",\"usa\"],\"articleSection\":[\"Innovation &amp; 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