4DM Developing a Near Real Time Community Flood Risk Awareness Tool for River Environments

4DM was awarded a DRDC CFP innovation project partnering with Government of Newfoundland and Labrador to conduct applied research and development to create a web-based tool to assess riverine flood risk to communities based on forecasted water levels. The information is generated in near real-time, at daily frequency, to provide early warning to authorities responsible for forecasting, mitigating, managing and responding to disasters related to floods. The study area used to develop the new standalone tool called “HydrologiX-Flood Alert” will be in Lower Churchill River in Labrador. The design goal of the project will be to implement a generic tool as well as process and interoperable standards and technology flexible to apply in other river systems in Canada.

4DM award IDEaS Project in Maritime Domain Awarness

4DM was awarded an Innovation Defence Excellence and Security (IDEas) project called Arctic Maritime Domain Awareness using Activity Based Intelligence – Data Drive Approach. The project is a proof of concept that involves the fusion of static and dynamic geospatial data that includes environmental, transportation, economic, social/culture and other datasets to build a dynamic data cube and apply AI modeling to determine unknown information around situation awareness in Canadian Arctic. Activity Based Intelligence is a multi-intelligent approach based on location information, temporal neutrality, data neutrality and integrating before exploiting to discover new information, anomalies and patterns of activities in the Canadian Arctic to support national security, environmental protection and public safety.

Development Churchill River Flood Forecasting System

4DM as part of consortium with KGS Group and Global Institute of Water Security was awarded a project on behalf of the Government of Newfoundland and Labrador to conduct a flood risk assessment and forecasting system. 4DM’s role in the project is to conduct land cover mapping using remote sensing and implement the flood forecasting system using the HydrologiX platform. In this project, 4DM designed and developed an automated web-based forecasting system for predicting hydrological flows, ice formation and ice breakup. Key elements of the development included the integration of hydrometeorological data feeds, tidal data, ice measurements, developing a hydrological model runner (HEC-HMS), a hydraulic ice model runner (RIVICE), as well as open water hydraulic model (HEC-RAS) to generate a daily 3-day water level forecast along the lower Churchill River.

The remote sensing work involved using Sentinel-2 data with supervised training machine learning approach to conduct the land classification and convert the data to Curve Numbers for hydrological Modeling. Data used in the project was Atmospherically corrected using Sen2Cor and mosaic into block regions for classification using Support Vector Machine (SVM).

Applying Artificial Intelligence for Surface Object Mapping Using Satelite Imagery

4DM has been awarded a major initiative to advance feature extraction technology using Artificial Intelligence for Surface Object Mapping using satellite imagery

Snowdrift Model and Mitigation for Highway 7 West of Carleton Place

4DM was subcontracted by the IBI Group on behalf of Ontario Ministry of Transportation to provide meteorological, snowdrift modeling and mitigation analysis to assess the severity of snow transport across section of Highway 7 near Carleton Place. 2D snow transport modeling was conducted based on return period and mitigation modeling assessed the snow fence requirements based on snow flux. The modeling considers the size, setup back, porosity, height and orientation. Oblique snow fencing was installed as outcome of the project.

Applying Satellite Techology for Maritime Domain Awarness

4DM as part of team lead by SSCL was awarded Canadian Space Agency – EOADP to investigate the value of space base earth observation data to provide persistent Maritime Domain Awareness. The project will include real time acquisition of optical (Dove, SkySat) and radar (RADARSAT-2) data with predictive modeling in areas of interest off west coast of Canada. 4DM will use patent pending technology to uniquely identify vessels, extracted vessel attributes and determine motion. Machine learning approach will be implemented to extract surface object characteristics. Information will be integrated into a predictive modeling tool called Timecaster developed by Maerospace for identify and validating the track of vessels with AIS data and “dark targets”. Goal is to demonstrate how the technologies can be applied to meet operational needs

Improve Hydrological Forecast Model

4DM has recently completed a project on assessing and improving performance of the Mattagami River Watershed Model (MRWM). The MRWM, developed by 4DM for Ontario Power Generation, is a WATFLOOD-based hydrological model which is designed to serve for (1) operational flow forecasting, implemented as part of the HydrologiX II web-based decision support system, and (2) water balance, climate impacts and water availability studies in the Mattagami River basin.

The project focused on advanced calibration and validation of the MRWM, incorporation of the Canadian Precipitation Analysis (CaPA) data, and robust automated model calibration using the OSTRICH optimization engine. As part of the optimization, we performed single objective calibration for streamflow, as well as two-objective model calibration for (a) streamflow (12 gauges) and (b) reservoir inflow (6 gauges) using a Pareto-based algorithm. Figure 1 and Figure 2 present examples of optimization results.

The calibrated model was able to achieve the average Nash-Sutcliffe model efficiency of 0.84 for streamflow (0.64 – 0.95 at individual gauges), and 0.77 for reservoir inflow (0.66 – 0.93 at individual gauges) over the 2002-2016 period.

The optimized MRWM is currently being deployed to the HydrologiX II system for use in operational flow and inflow forecasting. HydrologiX II has been utilized in the Mattagami River basin since 2014 for providing fully automated daily flow and inflow forecasts, situational awareness, monitoring, notification and data dissemination services.

4DM Conducts Visual Simulation Project

4DM has been awarded a visual simulation assessment project as subconsultant  to ARCADIS Canada. The simulation is part of Environmental Assessment on a structural change to hydro-electric transmission corridor. The outcome will be used to provide the public visual perspective of the changes to the transmission corridor for consultation.

4DM provides LiDAR expertise to Dam Safety Review

4DM as subconsultant to Sanchez Engineering was award the Palgrave Dam Safety Review for Toronto Region Conservation Authority. 4DM’s role will be to conduct terrestrial LiDAR and integrated with airborne LiDAR data to support structural, geotechnical and hydrotechnical analysis. Our team will support hydrology analysis and inundation mapping

4DM completes project to develop algorithms for near – real time flood extent and depth from RADARSAT-2 and LiDAR

4DM, in collaboration with C-Core, recently completed a project on the development of algorithms and software tools to support near real-time mapping of flooding, commissioned by Natural Resources Canada’s Emergency Geomatics Services (EGS). The objectives of the project were to (1) improve the accuracy of flood extent mapping by complementing an existing EGS open-water detection algorithm with specialized approaches for vegetated and urban areas and a GIS-based conflation post-processing, and (2) enable calculation of flood depth within the inundated area.

Within the scope of this project 4DM has developed an algorithm and a software toolset for performing conflation post-processing of remotely-sensed flood extent areas (open-water only, as well as combined with specialized vegetated/urban algorithms) to ensure the hydrologic validity of the resulting flood extent, and for determination of flood depth. 4DM also conducted research for the urban flood detection algorithm.

Preliminary results from the Richelieu and Red rivers indicate that the conflation post-processor significantly improves the accuracy of flood extent mapping in comparison to the existing open-water algorithm in areas where the flow pattern is primarily 1-D (well defined valleys) and high-resolution DEM is available. The approach currently does not perform as well in flat terrain where flow patterns are more 2-D in nature and/or DEM quality is lower.