1. 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 Virginia Travel

2. 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.
UH-DAIS

3. 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

4. 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
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

5. 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
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

6. Model Fusion and Meta Learning
USGS Models (
What model works best under which
circumstances?
Model Fusion
and
Meta-Learning
Data-driven Models
for the Watersheds
NOAA Models ( )
Our Project
UH-DAIS

7. 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.

8. Flood Risk Mapping Wharton County, Texas
Wharton County
Fire Chief
Wharton County
April 2016 Flooding

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

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

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

12. Automated Flood Risk Mapping

13. 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

14. 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

15. 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

16. 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

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

18. South Virginia: almost everything is NAVY
UH-DAIS

19. Virginia Aquarium
UH-DAIS

20. Virginia Museum of Contemporary Art
UH-DAIS

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

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