[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126957-en":3,"doc-seo-126957-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},126957,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","ACROPOLIS - A graphical user interface for classification of risk for off-stream reservoirs using machine learning","Potential risk identification due to off-stream reservoir failure typically relies on two-dimensional hydraulic models, which require substantial expertise, time, and financial resources. Many reservoir owners and administrations therefore face burdensome hazard assessment procedures. ACROPOLIS was developed to streamline this workflow by using a machine learning model to provide preliminary risk classification under Spanish regulations, without building a hydraulic model. The solution is integrated into a user-friendly interface to simplify adoption.","SoftwareX 26 (2024) 101657  \nContents lists available at ScienceDirect  \nSoftwareX  \njournal [homepage:](homepage: www.elsevier.com/locate/softx)[ www.elsevier.com/locate/softx](homepage: www.elsevier.com/locate/softx)  \n| ACROPOLIS: A graphical user interface for classification of risk for off-stream reservoirs using machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Nathalia Silva-Cancinoa, *, Fernando Salazar a, Ernest Blade´ba International Centre for Numerical Methods in Engineering (CIMNE), 08034 Barcelona, Spain b Flumen Institute-Universitat Polit`ecnica de Catalunya – CIMNE, 08034 Barcelona, Spain |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Off-stream reservoir Potential risk Machine learning Dam break |  | Potential risk identification due to off-stream reservoir failure typically requires the use of two-dimensional hydraulic models, which demands considerable effort in terms of expertise, time and financial resources. Unfortunately, not all reservoir owners have access to these resources, and the process of assessing hazard classifications for administrations can be burdensome. ACROPOLIS was developed to address this challenge, employing a Machine Learning model to provide a preliminary risk classification according to Spanish regulations for off-stream reservoirs without the necessity of building a hydraulic model. This approach has been integrated into a user-friendly interface, simplifying the process for users. |  |\n\n1. Motivation and significance  \nOff-stream reservoirs are water storage structures that are not influenced by hydrological conditions, as they are not connected to a flowing stream. However, in the event of failure, the damages can be significant due to their frequent proximity to urban areas and high elevations. In Spain, owners of off-stream reservoirs with a dike height of 5 m or more and a storage capacity exceeding 100,000 hm3 are required to classify the hazard associated with potential risks [1]. This hazard classification must adhere to the guidelines outlined in the Spanish Technical Guide for the Classification of Dams (STG) [2].  \nThe STG classification procedure introduced the \"complete method,\"which involves constructing two-dimensional (2D) hydraulic models to reproduce breach formation and flood propagation, considering velocities and depths, using software such as Iber [3], National Weather Service Dam-break Flood Forecasting Model (NWS DAMBRK) [4] and the Simplified Dam-Break Model (SMPDBK) [5]. In the STG the evaluation of areas of interest (AoI) around the structure is based on their maximum velocity and depth. According to Spanish regulations, any AoI with a maximum water depth equal to or greater than 1 m, a maximum velocity equal to or greater than 1 m/s, or a product of both greater than 0.5 m2/s is considered a potential risk to human life [1]. Using this identification, dams and off-stream reservoirs are classified as A, B, or C, with Category A indicating the highest hazard and Category C indicating the lowest.  \nAdditionally, Spanish regulations stipulate the requirement for an Emergency Plan when the structure is classified as A or B. This plan involves more detailed hydraulic modelling and justification of potential scenarios to address emergencies [6]. On the other hand, for off-stream reservoirs classified as C, only the classification assessment needs to be presented.  \nTherefore, the classification process demands significant resources, including financial means, engineering expertise, and time, which are often lacking for owners. To address this challenge, Silva-Cancino et al.(2022) [7] developed a machine learning (ML) algorithm that automates the risk identification process of the AoI. This solution eliminates the need to develop a two-dimensional hydraulic model, as suggested by the guidelines, offering a streamlined and efficient alternative.  \nIn this paper, we introduce ACROPOLIS, a user-friendly software designed to class","cbCainpIpTbJ8mgA","https://ap.wps.com/l/cbCainpIpTbJ8mgA","pdf",10756697,1,7,"English","en",105,"# Motivation and significance\n## Hazard classification in Spain\n## Complete method and hydraulic modelling requirements\n# Software description\n## GUI and ML computational core\n## Input parameters and risk aggregation","[{\"question\":\"Why are two-dimensional hydraulic models required for off-stream reservoir hazard classification?\",\"answer\":\"They reproduce breach formation and flood propagation and evaluate areas of interest using maximum velocity and water depth, following Spanish technical guidelines. This makes the process resource-intensive.\"},{\"question\":\"How does ACROPOLIS reduce the effort needed for risk classification?\",\"answer\":\"ACROPOLIS uses a machine learning model to classify risk using input parameters, avoiding the need to build a 2D hydraulic model. It then derives the overall hazard category by aggregating AoI results.\"},{\"question\":\"What does ACROPOLIS output for each area of interest and for the overall reservoir?\",\"answer\":\"For each AoI, the ML model labels it as “Risk” or “No Risk.” By aggregating the number of “Risk” AoIs, ACROPOLIS produces an overall hazard classification (A, B, or C).\"}]","ACROPOLIS - A graphical user interface for classification of risk for off-stream reservoirs using machine learning | PDF",1785935904,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"acropolis-a-graphical-user-interface-for-classification-of-risk-for-off-stream-reservoirs-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/acropolis-a-graphical-user-interface-for-classification-of-risk-for-off-stream-reservoirs-using-machine-learning/126957/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are two-dimensional hydraulic models required for off-stream reservoir hazard classification?","Question",{"text":76,"@type":77},"They reproduce breach formation and flood propagation and evaluate areas of interest using maximum velocity and water depth, following Spanish technical guidelines. This makes the process resource-intensive.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does ACROPOLIS reduce the effort needed for risk classification?",{"text":81,"@type":77},"ACROPOLIS uses a machine learning model to classify risk using input parameters, avoiding the need to build a 2D hydraulic model. It then derives the overall hazard category by aggregating AoI results.",{"name":83,"@type":74,"acceptedAnswer":84},"What does ACROPOLIS output for each area of interest and for the overall reservoir?",{"text":85,"@type":77},"For each AoI, the ML model labels it as “Risk” or “No Risk.” By aggregating the number of “Risk” AoIs, ACROPOLIS produces an overall hazard classification (A, B, or C).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":107,"slug":138},19,"General","general"]