Simulation and Visualization of Flood and Drought Events in Urban Areas

Consortium

Presagis Inc

Presagis Inc

Presagis is a Montreal-based software company that supplies the top 100 defense and aeronautic companies in the world with simulation and graphics software. Over the last decade, Presagis has built a strong reputation in helping create the complexity of the real world in a virtual one. Their deep understanding of the defense and aeronautic industries combined with expertise in synthetic environments, simulation & visualization, human-machine interfaces, and sensors positions them to meet today’s goals and prepare for tomorrow’s challenges. Today, Presagis is heavily investing into the research and innovation of virtual reality, artificial intelligence, and big data analysis. By leveraging their experience and recognizing emerging trends, their pioneering team of experts, former military personnel, and programmers are challenging the status quo and building tomorrow’s technology — today.

La ville de Terrebonne

Ville de Terrebonne

La ville de Terrebonne telle qu’on la connaît aujourd’hui est issue de la fusion en 2001 des municipalités de Terrebonne, de Lachenaie et de La Plaine. Terrebonne est la dixième ville en importance du Québec. Son territoire s’étend au total sur près de 160 km2 avec une longueur avec 27,8 km entre son point le plus à l’est et son point le plus à l’ouest. La municipalité est traversée du nord au sud par les autoroutes 25 et 40, soit deux axes structurants pour les villes de la couronne nord de Montréal, et plus généralement pour les régions administratives des Laurentides et de Lanaudière. En étant située entre Laval et l’Est de Montréal et en zone limitrophe des Basses-Laurentides, Terrebonne jouit d’une localisation stratégique qui offre un fort potentiel de développement économique. Par ailleurs, Terrebonne est bordée par la belle richesse naturelle qu’offre la rivière des Mille-Îles. La municipalité vise l’atteinte d’un bon équilibre entre son côté nature et son côté urbain au profit d’un milieu de vie sain pour ses citoyens.

Concordia University, Montreal, Quebec

Immersive & Creative Technologies Lab

The Immersive and Creative Technologies lab (ICT lab) was established in late 2011 as a premier research lab, committed to fostering academic excellence, groundbreaking research, and innovative solutions within the field of Computer Science. Our talented team of researchers concentrate on specialized areas such as computer vision, computer graphics, virtual/augmented reality, and creative technologies, while exploring their applications across a diverse array of disciplines. At the ICT Lab, we strive to achieve ambitious long-term objectives that are centered around the development of highly realistic virtual environments. Our primary objectives include (a) creating virtual worlds that are virtually indistinguishable from the real-world locations they represent, and (b) employing these sophisticated digital twins to produce a wide range of impactful visualizations for various applications. Through our dedication to academic rigor, inventive research, and creative problem-solving, we aim to propel the boundaries of technological innovation and contribute to the advancement of human knowledge.

Researchers

People who have worked on the project; sorted according to graduation date where applicable:

Shubham Rajeev Punekar - Concordia (PhD)

Naghmeh Shafiee Roudbari - Concordia (PhD)

Viktoriya Markutsa - Concordia (MSc)

Yashas Joshi - Concordia (PhD) - graduated

Anh Phuong Tran - Ville de Terrebonne (Architecte de solutions)

Philippe Hamel - Ville de Terrebonne (Chef de section, sécurité organisationnelle et réseautique)

Rémi Asselin - Ville de Terrebonne (Directeur, Direction des technologies de l'information)

Sacha Lepretre - Presagis Inc (CTO)

Charalambos Poullis - Concordia (PI)

Research Objectives

Computational Fluid Dynamics

AI-based Fluid

Simulating Floods for Risk Assessment and Evaluation of Countermeasures.

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Publications

TBA

From Data to Action: Flood Forecasting Leveraging Graph Neural Networks and Digital Twin Visualization

Naghmeh Shafiee Roudbari*, Shubham Rajeev Punekar*, Zachary Patterson, Ursula Eicker, Charalambos Poullis
TBA
Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks.
Recent efforts in hydrology have aimed at predicting water flow, floods, and quality, yet most methodologies overlook the influence of adjacent areas and lack advanced visualization for water level assessment. Our contribution is twofold: firstly, we introduce a graph neural network model equipped with a graph learning module to capture the interconnections of water systems and the connectivity between stations to predict future water levels. Secondly, we develop a simulation prototype offering visual insights for decision-making in disaster prevention and policy-making. This prototype visualizes predicted water levels and facilitates data analysis using decades of historical information. Focusing on the Greater Montreal Area (GMA), particularly Terrebonne, Quebec, Canada, we apply our model and prototype to demonstrate a comprehensive method for assessing flood impacts. By utilizing a digital twin of Terrebonne, our simulation tool allows users to interactively modify the landscape and simulate various flood scenarios, thereby providing valuable insights into preventive strategies. This research aims to enhance water level prediction and evaluation of preventive measures, setting a benchmark for similar applications across different geographic areas.
ICMLA2023

TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting

Naghmeh Shafiee Roudbari, Charalambos Poullis, Zachary Patterson, Ursula Eicker
ICMLA 2023
The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control.
However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to develop models that represent the relationships between other system components. In recent years, numerous hydrological applications have been studied, including streamflow prediction, flood forecasting, and water quality prediction. Existing methods are unable to model the influence of adjacent regions between pairs of variables. In this paper, we propose a spatiotemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder using an efficient version of the attention mechanism.

Contact

Charalambos Poullis
Immersive and Creative Technologies Lab
Department of Computer Science and Software Engineering
Concordia University
1455 de Maisonneuve Blvd. West, ER 925,
Montréal, Québec,
Canada, H3G 1M8

charalambos [at] poullis [dot] org

+1 514 848 2424 x3019