Current research projects involving members of LEMON

International Grants

National (ANR) Grants

National Projects

Industrial collaborations

Former research projects involving members of LEMON

International Grants

National (ANR) Grants

National Projects

Industrial collaborations

ANR Project MUFFINS (2022-2026)

Title: MUltiscale Flood Forecasting with INnovating Solutions (ANR-21-CE04-0021-05).
Partners: INRAE, IMT, Univ Eiffel, Cerema IMFT, CCR, Météo/SPCME, SCHAPI
The objective of the MUFFINS project is to develop new accurate and computationally efficient flood forecasting approaches, enabling transferring information between modelings (meteo-hydrology-hydraulic-damage) and scales (from local runoff generation over areas lesser than 1km2 to flood propagation on catchments of thousands km2), and taking advantage of innovative data (in situ, remote observation, opportunistic) to reduce forecasts uncertainties.
More precisely, the MUFFINS project is expected to provide significant advances on the following aspects:
• Understanding and specifying the different requirements and expectations of actors having to take decisions at different times: hours before the crisis (i.e.: internal pre-alerts in a rescue service for preparedness actions), during the crisis (i.e.: immediate rescue of people at risk) and hours after (i.e: first loss assessments by insurance companies);
• Design of new methodologies and tools, in adequacy with user’s needs, for next generation of spatially distributed flood warning methods (improved handling of hydro-meteorological inputs in high uncertainty & low predictibility contexts, seamless regionalizable and multi-scale hydrological-hydraulic modeling chains, fast & accurate computations, flood impact mapping, enhanced synergies with multi-source data);
• Integration of information from multi-source data for improving the accuracy of integrated chains and their adequacy with flood impacts mapping;
• Demonstrators of the methods on Mediterranean catchments with multi-scale issues and rich datasets

ANR Project CROQUIS (2022-2025)

Title: Collecting, Representing, cOmpleting, merging and Querying heterogeneous and UncertaIn waStewater and stormwater network data.
Partners: CRIL, I3S, HSM

Urban water network managers are increasingly faced with the analysis of massive heterogeneous data/information (imprecise and uncertain geographical data, digital/analogue maps, etc.). Intelligent analysis of this data is a challenge that needs to be addressed in order to obtain accurate maps of networks, monitor environmental variables and identify areas for intervention.

The multi-disciplinary CROQUIS project is set in this context where researchers in Water Sciences and Artificial Intelligence will join their efforts to propose new methods for the representation, completion, fusion, archiving, repair and interrogation of heterogeneous data describing wastewater networks.

The consortium, composed of two partners in Artificial Intelligence (CRIL: Centre de Recherche en Informatique de Lens, project coordinator, and I3S: Laboratoire d’Informatique, Signaux et Systèmes de Sophia Antipolis) and one partner in Water Sciences (HSM: HydroSciences Montpellier), offers complementary skills that are essential to the achievement of the objectives of this project.

ANR Project McLaren (2020-2024)

Title: Machine Learning and Risk Evaluation (ANR-20-CE23-0011).
Partners: University Côte d’Azur, University of Montpellier, INRAE, Inria and CNRS.

The overall objective of the project is to bring significant innovations in the fields of statistical learning (and more particularly unsupervised learning, i.e. clustering) and risk assessment, linked by the problem of level sets estimation. To achieve this, the project has set three specific objectives:

  • Improve and propose quantization or depth based clustering methods for this new type of data (Machine Learning) ;
  • Address the topic of risk evaluation ;
  • Link these two axis by a transversal axis “level sets estimation”.

European Staff-Exchange Project STARWARS (2023-2025)

Title: STormwAteR and WastewAteR networkS heterogeneous data AI-driven management.
Partner: CRIL (Centre de Recherche en Informatique de Lens); HydroSciences Montpellier; ISTI (Institute for Information Science and Technology Institute), Pisa, Italy; CAIR (Centre for Artificial Intelligence Research), Cape Town, South Africa; LSIA (Intelligent Systems and Applications Lab), Fez, Morocco; CICT (College of Information and Communication Technology), Can Tho, Vietnam.
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.

European Project CASCADE (2018-2022)

Title: Combining earth observation with a large scale model cascade for assessing flood hazard at high spatial resolution.
Partner: LIST (Luxembourg Institute of Science and Technology)
The CASCADE project aims at developing a Satellite Earth Observation (SEO)-based modelling framework that enables an assessment of flood hazard at large scale and high spatial resolution. The project intends to unlock the potential offered by recent developments in terms of high performance computing, parsimonious and efficient hydrological and hydraulic models, as well as the availability of globally coherent remote sensing data. By using SEO data and other globally and freely available data sets as default data for driving and parameterizing the model, the project aims at developing a modelling solution that is no longer relying entirely on the availability of long records of reliable in situ observations. Such developments are considered a pre-requisite for hydrology-related disaster risk reduction worldwide.

Project ANR DEUFI (2019-2023)

Title: Detailing Urban Flood Impact
Partners: GEAU, Artelia, LMFA, Cerema, ULiège, ICube
Although urban floods were largely investigated, the flow exchanges between streets and buildings are poorly documented in the laboratory and in the field. DEUFI project fills this gap focusing on the hydraulic processes inside and outside one urban block and assessing how this knowledge can be useful to estimate the damages and the number of fatalities.

Project ANR GAMBAS (2019-2023)

Title: Generating Advances in Modeling Biodiversity And ecosystem Services
Partners: CIRAD, INRAE, LECA, MUSEUM National d’Histoire Naturelle, IMAG, Université Paris-Saclay
GAMBAS gathers a collective comprised of quantitative ecologists and mathematicians with aspirations in ecology . An advisory committee composed of representatives of the French government and civil societies will support these investigators. Together, they have set the goal of expanding JSDMs and to promote their use in community ecology, biogeography, and conservation ecology. Multiple datasets will contribute to fulfill the objectives of GAMBAS, including terrestrial (birds, temperate and tropical forests) and aquatic (freshwater functional diversity) biological communities.

Inria Challenge SURF (2019-2023)

Title: Sea Uncertainty Representation and Forecast
Partners: Inria, BRGM, SHOM, Ifremer
Understanding the dynamics of the oceans is a key scientific issue. It has many applications in coastal zone management, the regulation of maritime traffic and the prevention of ecological, meteorological and industrial risks. While scientific computing is now one of the most widely-used tools to explain or predict changes in the ocean, simulation tools are still reserved for specific purposes. The SURF project brings together several Inria teams that are pooling their expertise to develop a common platform for computing oceanic flows in littoral and coastal zones.

Project AMANDE (Partenariat Hubert Curien)

PHC project Amande (2019-2021): stochastic and semi-parametric approaches combined to remote sensing for the study of the water stress

Collaboration with Berger-Levrault

Title: Data fusion for wastewater network mapping
Partners: Berger-Levrault, LSIA (Univ. SMBA, Fes Morocco)
This work will concentrate first on putting forward, adapting and using data fusion methods to combine, on the one hand, the attributes of the different objects that form a network (depth, diameter of a pipe etc.) and on the other hand, the georeferenced data which provide the position of these elements. Moreover, the nodes interconnection and structure complexity of this type of networks requires the development of efficient updating methods and their propagation.

Project ANR ANSWER (2017-2021)

Title: Analysis and Numerical Simulation of Water Ecosystems in Response to anthropogenic environmental changes
Partners: MISTEA, LEESU, iEES Paris, LBE, NIGLAS, Chineese Academy of Sciences
The increase in the occurence of cyanobacterial blooms threaten the ecosystem services provided by lakes and reservoirs worldwide. In this French-Chinese collaborative project, researchers of various fields will work together to develop an integrative platform for lake ecosystems modeling, which will include some tools of data management and knowledge representation, some models and some methods of analysis.

Project FRAISE (CNRS funding, Lefe Manu)

LEFE MANU Fraise (2019-2021): rainfall forcing by stochastic simulation for hydrological impact studies from dry periods to extreme events

Collaboration with IRT Saint-Exupéry

This collaboration is intended to implement and evaluate the relevance of AI tools in physical and numerical modeling. These physical and numerical methods, exploited within the framework of problems whose physical processes are well controlled, are however limited by computation times and the resolution of mesh-to-mesh numerical problems. These classical methods therefore require a multiplication of technical means (such as the processor computing capabilities, or parallelization) when downscaling or parallelization is required. spatial or temporal refinement is required. Conversely, an upscaling process can be undertaken in order to spatially simplify the resolution of physical processes and mathematical tools through a coarser mesh for example.

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