Cities Becoming Smarter: Preparing for Regional Resilience

Transcription

1 Cities Becoming Smarter: Preparing for Regional Resilience Sridhar Katragadda Systems Analyst City of Virginia Beach Dr. Jon Derek Loftis Asst. Research Scientist VA Inst. of Marine Science Tammie Organski GIS Manager City of Newport News ESRI User Conference, July 11, 2017 Transparency and Accountability with GIS

2 Abstract Rapid progress in Internet of Things (IoT) and the cloud environment with cutting-edge science, is bringing local governments together through Global Cities Team Challenge (GCTC) to create smart cities, and smarter region, thus striving toward a common goal for community resilience in a proactive way. Address challenges faced with recurring weather issues, and bringing analytics to raise citizen awareness, in a scalable way using ESRI and other emerging technologies.

3 Outline 1. Introduction Hydro. vs. Bathtub Models vs. FEMA Floodplains StormSense Sensors and Sub-Grid Modeling Approach 2. Methods StormSense Data Management Model Grid Development in Hampton Roads 3. Results & Discussion 4. Conclusions

4 1. Introduction Hydro. vs. Bathtub Models vs. FEMA Floodplains StormSense Sensors and Sub-Grid Modeling Approach

5 Introduction The Hampton Roads region is the second-largest population center in the U.S. at risk from sea level rise (Boon, Brubaker and Forrest, 2010; Mitchell et al., 2013) More than 400,000 properties exposed to flood or storm surge inundation (CoreLogic, 2015) Population of over 1.7 million people, living and traveling on roads exposed to severe and increasing frequent chronic nuisance flooding (Ezer and Atkinson, 2014) Recent Hurricane Sandy that grazed the region just off the coast was a reminder of the significant vulnerability of the coastline

6 Community Resilience Proactive Virginia Beach, VA Several cities around the Hampton Roads region are adjacent to each other and traffic can become congested around several bridges and tunnels. It is home of the one of the world s largest naval bases Existing flood communication and messaging systems have not yet responded to the changing risk patterns brought by sea level rise and have not been able to meet the needs of diverse at-risk communications audiences (IoT sensors and predictive models can help) A better understanding of flood risk perception, information-seeking behavior and decision-making can inform the development of new communications tools and flood risk messaging

7 GCTC - USignite, National Institute of Standards and Technology (NIST) Smart Cities and Smarter Region Bringing science into the city Concepts and Demonstrations Virginia Beach Proposed two projects as part of the Capital Improvement Program (CIP) Newport News and Virginia Institute Of Marine Science (VIMS) participated in Global Cities Team Challenge (GCTC) Competition Virginia Beach, Norfolk and surrounding cities Collaborations with the region, technical knowhow and Austin Expo 2016 Demonstration Acquired Funding from NIST as an Replicable Smart City Technologies (RSCT) Grant One CIP project was funded for Water Level Sensors (IoT) in Virginia Beach

8 GCTC Expo 2017 The Largest Smart City Event hosted by the US Federal Government August 28-29, 2017, Walter E. Washington Convention Center (Washington DC) Monday (8/28) & Tuesday (8/29) Morning: Plenary keynotes and SuperCluster presentations/highlights Afternoon: Exhibitions and breakout presentations from action clusters Federal government keynotes Mayor s keynote panel 6 SuperClusters, 90+ Action Clusters presentations and exhibits 100+ Cities and communities from around the world 400+ Companies, universities, non-profits, and other smart city organizations Attendees No registration fee. Limited travel support available for city officials speaking as a member of an action cluster or SuperCluster. Visit To become an Action Cluster, contact Sokwoo.rhee@nist.gov

9 Project Partners (as of July 2017): StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide

10 2. Methods StormSense Data Management Model Grid Development in Hampton Roads

11 Regional Datasets Public Works and USGS Nine Tide Gauges July 2016 Virginia Beach Public Works and Public Utilities Rain Gauges Gauges > 32 Hampton Roads Sanitation District 74 Rain Gauges Radar and Ultrasonic Water Level Gauges Virginia Beach CIP More than 20 Water Level Sensors Newport News - GCTC and VDEM Funding 8 Norfolk GCTC and City Funding - 6 Reviewed the below studies Iowa Flood Center ( Philippines (

12 IoT Sensors Communication Virginia Beach has nine USGS tide gauges that were installed in July, 2016 as part of Public Works project Additional ultrasonic, radar water level sensors are in the process of being installed over a two year period in the region through city, NIST RSCT and VDEM funding Research and implementation of different sensors were reviewed for selection Four or more types of Ultrasonic sensor as well as radar sensors are being tested, calibrated for several parameters and autonomous nature as part of the project for a few months Latest machine-to-machine (M2M) communication technology by Ingenu will also be tested as part of sensor communication to the cloud

13 IoT Sensors Communication

14 IoT Sensors Valarm Senix Communication Yocto Hub - Wireless - g / WiFi for Industrial IoT Power Virtual Hub Measuring Rain Gauges station (Detachable), shown with Connecting the ultrasonic Tube, sensor Water Water is poured into one rain gauge Filling Station

15 Cloud Environment Why Cloud Provide aggregation support of disparate datasets from surrounding cities such as leverage existing sensors in Public Utilities, Public Works such as rain gauges on pump stations etc. regionally Provide ability to obtain data from smaller cities, legacy systems and vendor technologies seamlessly Provide flexible on premise implementation capability (JSON output ) Provide near real-time data wherever applicable Provide data repository for short term Provide outputs to support mapping platforms seamlessly Provide subscription notification services to the citizens Provide a replicable solution that is scalable Provide voice, AI and Analytics

16 Integration Roadmap Cities Scada Telog S3 Sutro n IoT Sensors Node.js (npm) Hydrodynamic/ Precipitation Model Other Cloud/ MEAN Stack Mapping (ArcGIS and Other) Predictive Analytics

17 Sensor Network and Modeling Approach StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide Observations & Predictions 6-min automated retrieval script IoT Stream Gauge Network StormSense Hydrodynamic Forecast Model Server StormSense Web Portal stormsense.com

18 StormSense Methods 1.amazonaws.com/ Data Management Absorb and standardize disparate data sources based upon data type and measurement interval Development of web-based GUIs for adding, removing, and configuring data Accomplished via Angular.js At a minimum, will provide command line and simple file editing approaches and documentation for configuration management.

19 StormSense Data Management Priority Site Sensor Type Power Source 1 16th St. Bridge over Salters Creek Ultrasonic Sonar Data Transmission Sensor Cost Data Transmission Cost $7.00 Solar/Battery Cellular Modem $2, (Verizon 5MB plan)

20 StormSense Data Management Data Storage and Access Original data sources will be considered the systems of record StormSense will serve as a bridge application or middleware transform service Standardize incoming data streams into unified data formats (XML, JSON, GeoJSON, HTML) following documented schemas. Provide query capabilities across different sensor/data types Format and queue boundary and initial conditions for StormSense street-level hydrodynamic model

21 StormSense Data Management Water levels extracted from grid cells with water level observations Perl and python scripts run in the background to produce geotiff rasters of water level and flood heights (water level- land elevation) for each 6-minute interval Spatial outputs are prepared as.kml files and javascript-layers for production of open layers maps, Google Maps, and Google Earth animations.

22 StormSense Data Management

23 Replicable Solutions RSCT

24 Model Grid Development with Lidar-Derived DEM StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide Middle Towne Arch Grandy Park Moseley Creek

25 3. Results & Discussion

26 A Hurricane Irene (Max. Flood Extent Map w/ Contours)

27 B Hurricane Irene (Time-Aware Animation)

28 StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide B C A RMSE = 3.18 cm Hurricane Hermine Maximum Flooding Extents in Norfolk (Sept. 2-3) Site Locations and Time Series Comparisons with Water Level Sensors

29 Hurricane Hermine Max. Flood Extents StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide Site A Carroll/Cambridge Crescent in Larchmont Neighborhood Maximum Flooding Extent Avg. Horizontal Dist. Diff. = 3.92m (n = 74 points)

30 Hurricane Hermine Max. Flood Extents StormSense Project Forecasting Flooding from Storm Surge, Rain, and Tide Site B Richmond Crescent in Larchmont Neighborhood Maximum Flooding Extent Avg. Horizontal Dist. Diff. =6.72 (n = 65 points)

31 ESRI Mapping Aggregates disparate datasets from surrounding cities to leverage existing sensors in Public Utilities, Public Works such as rain gauges etc. regionally Provide near real-time data dependent Provide data repository for short term Provide outputs to support mapping platforms seamlessly Provide subscription notification services to the citizens Provide a replicable solution that is scalable Provide flexible on premise implementation capability

32 ArcGIS Carto Other ESRI ArcGIS CartoCarto Online ESRI ArcGIS Online

33 4. Conclusions The sub-grid model forecasted tidal flooding during Hurricane Hermine in Sept and was well validated via tide gauges and Sea Level Rise App GPS extent data: Vertical Accuracy: aggregate RMSE of 3.19 cm (n=5; 416ts each) Horizontal Accuracy: distance diff. of m (n=263; GPS pts) Through StormSense, 24 more sensors are planned for installation throughout Hampton Roads by summer s end, courtesy of NIST RSCT funds, VDEM, & Virginia Beach CIP.

34 4. Conclusions (cont d) Amazon Web Services will provide a resilientr portal to handle significant load of page requests to access data during peak traffic periods coincident with flooding The model results being served up on ESRI REST timeaware feature services will help aid in understanding of model results for decision makers, emergency responders and the general public

35 Moving forward In the future, these data validations and ESRI s new Drone2Map platform may also serve as a platform where SLR GPS flooding extent data may be entered by trained, registered users for inter-comparison between different inundation models in the AWS.

36 Thanks for attending our presentation. I hope some ideas that were presented here are worth taking back with you. Your feedback is welcomed and it may help us think of new approaches Katragadda Sridhar skatraga@vbgov.com Dr. J. Derek Loftis jdloftis@vims.edu Tammie Organski torganski@nnva.com