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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
HEОбмен на изследователи2023–2026

STARWARS · STormwAteR and WastewAteR networkS heterogeneous data AI-driven management

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2023-01-01 → 2026-12-31
Финансиране от ЕС
1 196 000 €
Участници
8
Схема
HORIZON-TMA-MSCA-SE

Линиите свързват координатора с партньорите.

Накратко на български

Изкуственият интелект се използва за обединяване на различни данни от канализационните мрежи, като например видеоинспекции на тръби и цифрови карти. Това помага за по-доброто управление на мрежите, когато информацията е непълна, противоречива или разпръсната в различни формати.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

STormwAteR and WastewAteR networkS heterogeneous data AI-driven management

The multidisciplinary STARWARS project (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management) aims to address the challenges associated with managing heterogeneous geospatial data by exploiting and developing artificial intelligence (AI) tools. The solutions developed within this framework are designed to be generic while using wastewater networks as an illustrative application for issues such as data completion, exploiting the complementarity of multi-source information, and managing the diversity of data formats, based on real data collected. Wastewater networks encompass a wide variety of data that includes geographic information systems (GIS), pipe video inspections, analog or digital maps, as well as textual or .pdf intervention reports. The available information, however, is often fragmented, imprecise, dynamic, and sometimes contradictory, coming from multiple sources. The objective of the STARWARS project is to develop AI-driven solutions capable of representing, managing, merging, explaining, and querying complex data, while considering their diversity and unique characteristics. These solutions are based on innovative models and tools that use logical and graph-based representations of heterogeneous data. Specifically, we aim to represent different data types — such as geographic information systems, ITV inspection videos, and maps — as annotated graphs, while addressing the uncertainty arising from incomplete information, undetected elements (e.g., street names in the videos), or missing crucial data (e.g., manhole details in GIS). The approaches proposed within the STARWARS framework are designed to integrate and combine uncertain, conflicting, and dynamic data to respond effectively to queries, even when the available information is incomplete or insufficient, while also considering the explainability of results, ensuring that the models incorporate mechanisms to justify their outputs. This project also aims at promoting knowledge exchange between partners of this project. It aims to produce new knowledge and to promote knowledge exchange between EU and non-EU partners, with a strong will and a plan to encourage knowledge sharing between researchers involved in this STARWARS project. Members of this consortium have very complementary skills, key to the realization of this project, in research areas of Water Sciences and Artificial Intelligence.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Public and private stakeholders of the wastewater and stormwater sectors are increasingly faced with large quantities and multiple sources of information/data of different nature: databases of factual data, geographical data, various types of images, digital and analogue maps, intervention reports, incomplete and imprecise data (on locations and the geometric features of networks), evolving and conflicting data (from different eras and sources), etc. Obtaining accurate and updated information on the underground wastewater and stormwater networks is a challenge and a cumbersome task, especially in cities undergoing urban expansion. Within this context, the main objective of this multidisciplinary project, STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management), is to address this challenge by providing novel proposals for the management of heterogeneous data in stormwater and wastewater networks. The STARWARS project aims to bring together researchers from the AI and Water Sciences communities in order to enhance the emergence of new practical solutions for representing, managing, modelling, merging, completing, reasoning, explaining and query answering over data of different forms pertaining to stormwater and wastewater networks. The project is implemented through five work packages (WP). The first four WP concern research developments of new AI methodologies for managing heterogeneous stormwater and wastewater networks’ data. The fifth WP is dedicated to project management and dissemination activities.The second objective of the project is to produce new knowledge and to promote knowledge exchange, with a strong will and a plan to encourage knowledge sharing between the researchers involved in this STARWARS project. The scheduled secondment plan is designed with the aim of maximizing knowledge transfer and training between the two fields of Water Sciences and AI and thus facilitating the achievement of the project objectives.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз