Meeting 03/24/2017 Short Overview UH-DAIS Lab Research Waterlevel Prediction and Flood Early Warning Systems Flood Risk Mapping (in collaboration with UT Center for Research in Water Resources) Expert System for Flood Risk Mapping Flooding Funding Opportunities NSF Planning Proposal Flooding People 8. March 16-20 Virginia Travel

Predicting, Mapping, and Understanding Flooding Research Tasks: Predict Water Levels Flood Risk Mapping Early Warning System Understand Flooding People: Christariny Hutapea, Yue Cao, Chong Wang, and Yongli Zhang. http://www.harriscountyfws.org/ UH-DAIS

2. A Generic DAG-Based Chaining Approach for WLP R(t),R(t-1),…(Rainfall) W(t), W(t-1),…(Water-level) V(t),V(t-1) (Stream Velocity) S(t), S(t-1)… (Soil Moisture) D(t), D(t-1),…(Discharge) Raw Data DAG of Measuring Points Prediction Scenario Mapping Tool currently under development at UH Model Execution Framework Data Sets (one for each Measuring Point) Single Target Prediction System uses f2 f1 f3 f4 DAG Models (one for each Measuring Point) off the shelf

Training Challenges for Data Driven WLP Approaches Need to extract historic water level, dischargeand rainfall data from USGS National Water Information System, Austin Flood Early Warning System, HydroMet, HCFCD EWS, Seattle… Extract historical soil moisture data from NASA NLDaS https://ldas.gsfc.nasa.gov/nldas/NLDASsoils.php Creating stream velocity data: use USGS, use the National Water Model predictions to create velocity data in training sets,…?!? Remark: Creating a Challenging Water Level Prediction Benchmark to Test and Compare Various WLP Systems is important! W3,t=f3( W1,t, W3,t-1, R3,t, V3,t-1, S3,t-1) Prediction Scenario f2 f1 f3 f4 DAG UH-DAIS

Using Learned Models to Make Water Level Predictions Rain Forecast has to be fed into the system; we do not plan to develop a rain forecasting system… Soil Moisture has also be fed into the system or alternatively be predicted; there might be some useful work at NASA SMAP https://smap.jpl.nasa.gov/ Stream Velocity has also be fed into the system or alternatively predicted Those data have to be available in real-time or “almost real-time” W3,t=f3( W1,t, W3,t-1, R3,t, V3,t-1, S3,t-1) Prediction Scenario f2 f1 f3 f4 DAG UH-DAIS

Model Fusion and Meta Learning USGS Models (http://water.usgs.gov/nrp/software.php) What model works best under which circumstances? Model Fusion and Meta-Learning Data-driven Models for the Watersheds NOAA Models (http://water.noaa.gov/about/nwm ) Our Project UH-DAIS

3. Automated Flood Risk Mapping Austin Fire First Responder Vehicle Flood Risk Map Creating these maps is a lot work and requires GIS and hydrology knowledge.

Flood Risk Mapping Wharton County, Texas http://abc13.com/news/wharton-mayor-calls-for-voluntary-evacuation/744958/ Wharton County Fire Chief Wharton County April 2016 Flooding

Height Above Nearest Drainage (HAND) developed by: UT Austin Center for Research in Water Resources Flood HAND Normal

Texas Hand Map (7,600,000 Address Points) 237 counties mapped 16 CSEC counties geocoded 1 non-CSEC county geocoded

Hand-based Flood Risk Assessment Austin Metropolitan Area (Williamson, Travis, and Hays County)

Automated Flood Risk Mapping

Polygon Analysis for Automated Flood Risk Mapping FEMA Warning Zones Find Correspondence Find Agreement, Combine, … Hand Value Multi-Contour Maps Austin Fire First Response Vehicle Flood Risk Map UH-DAIS

Analyzing Different Polygonal Data that Describe Levels of Flood Risks Benefits and Objectives: Flood Risk Map Validation Flood Risk Map Standardization Creating Flood Risk Maps Automatically Mapping Agreement/Disagreement with Respect to Different Flood Risk Models. Understanding the weakness/strength of particular methods Creating “combined” flood risk models Dynamic Flood Risk Maps that adapt their appearance based on contextual information UH-DAIS

4. Expert Systems for Flood Risk Mapping Local Information (Watersheds, Elevation Maps,…) Expert System creates User (not an expert in GIS and hydrology) Flood Risk Mapping Knowledge Base

6. NSF Planning Proposal (Eick&Peres) Design of a Generic Water Level Prediction and Flood Early Warning System Overview: Flood is the most hazardous natural disasters in the world …Consequently, many US cities developed sensor based early warning systems that collect water level and other data in real-time…One major research goal of the proposal is the development of a flood early warning system that provides the capability to predict future water levels. To accomplish this goal novel data-driven water level prediction techniques that interpolate the past into the future will be investigated, compared and fused with classical hydrological models. However, for a flood early warning system to be useful, it is critical to ensure that the needs of the stakeholders interacting with such a system are considered in the system design. Investigating the behavioral aspects and human-computer interaction aspects of stakeholders using flood warning systems is the second major research goal of this proposal. To accomplish these two goals, we plan to assemble a strong research team that consists of surface hydrologists and members of organizations, such as NOAA, that develop water level prediction models and representatives of flood control districts and emergency management departments of several US cities. UH-DAIS

Great Dismal Swamp, Virginia 8. Virginia Travel Great Dismal Swamp, Virginia Data Analysis and Intelligent Systems Lab

South Virginia: almost everything is NAVY UH-DAIS

Virginia Aquarium https://www.virginiaaquarium.com/ UH-DAIS

Virginia Museum of Contemporary Art http://www.virginiamoca.org/ UH-DAIS

Viriginia Beach Shamrock Weekend March 17-19, 2017 UH-DAIS

Virginia Beach Shamrock Races March 18, 2017 trishula trident March 18, 2000