This project has received funding from the European Union’s Horizon 2020 research and innovation program under the MSCA (Marie Sklodowska-Curie Actions, Staff Exchanges)-SE (Staff Exchanges) grant agreement
No 101086252.
Grant Agreement ID: 101086252
EC Signature Data: 10 October 2022
Start date: 1 January 2023 End date: 31 December 2026
Funded under: Marie Skłodowska-Curie Actions (MSCA)
Coordinated by: CNRS, France
STARWARS
STormwAteR and WastewAteR networkS heterogeneous data AI-driven management
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 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) as.
Objects in Project
Work packages
WP1: Data Collection and Data Completion
Objective 1.1 consists in identifying data sources and collecting data/knowledge. Objective 1.2 deals with unstructured data and building terminological knowledge bases. Objective 1.3 consists in using machine learning and inference algorithms, enhanced by kn
WP2: Unreliable and Heterogeneous Data Modelling
Objective 2.1 concerns the definition languages for representing different forms of data and knowledge. Objective 2.2 aims at studying the problem of heterogeneity of data description models or uncertainty frameworks that may happen when the different information sources, to be combined, do not share a common data description language.
WP3: Practical Merging, Inconsistency and Clustering
Objective 3.1 deals with the problem of combining, knowledge dynamic and integration. Objective 3.2 addresses the issue of handling conflicts which is a central feature in fusion and an increasingly important topic within AI. Objective 3.3 relates to the development of classification and graph clustering methods in the context of wastewater and stormwater networks data.
WP4: Tractable Query-answering, Explainability, Algorithms and Validations
Objective 4.1 aims to develop tractable query-answering mechanisms that take into account uncertainties and inconsistencies in information/data. It also aims to propose different strategies for explainability, including clustering solutions and argumentations, that go beyond mere weighted answer representations.Objective 4.2 concerns the building of datasets and the development of algorithms and tools that require empirical validations where experiments will be carried out on real datasets (collected in WP1 and undergoing processing steps defined in WP2-3)
WP5: Project management, Communication, Dissemination and Training
This is fully dedicated to the project management, training activities, and the dissemination of the research results. It covers in particular i) training sessions and communication of the research results, ii) two summer schools (one on the heterogeneity of wastewater and stormwater networks data and the other on data fusion), iii) two AI specialized workshops (one on information fusion and the other on reasoning with uncertainty), iv) a side event (for the Water Science community) and v) a final workshop aimed at a large audience.
Wastewater and Stormwater Data to Modelling
Dataset and Knowledge
Within this context, the scientific guiding principle of this multidisciplinary project, STARWARS (STormwAteR and WastewAteR networkS heterogeneous data AI-driven management), is to address the challenges identified above by providing novel proposals for the management of heterogeneous data in stormwater and wastewater networks.
Heterogeneous wastewater and stormwater network data first refer to data of different natures such as datasets of factual data, geographical data, various types of images, digital maps (e.g., Figure 3), analogue maps, intervention reports, etc
