1. Meeting 02/27/2017 Short Overview UH-DAIS Lab Research
Waterlevel Prediction and Flood Early Warning Systems
Flood Risk Assessment (in collaboration with UT Center for Research in Water Resources)
Flooding Funding Opportunities
Feedback Concerning an NSF Planning Proposal
Austin Fire First Responder Vehicle Flood Risk Maps (likely the theme of meeting with Jorge at 9:30a one floor up)

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)
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 and rainfall data from USGS National Water Information System, Austin Flood Early Warning System, HydroMet, HCFCD EWS, Seattle…
Extract historical soil moisture data from NASA NALDS !?!
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. Flood Risk Mapping
Integrating Polygonal Data 22t2hat Describe Levels of Flood Flood Risk Mapping
FEMA Warning
Zones
Contour
Lines for
Hand
Value15
Find: Correspondence
Find Agreement,
Combine, …
Austin Fire
First Response
Vehicle Flood
Risk Map
UH-DAIS

8. Height Above Nearest Drainage (HAND)
Flooding occurs when Water Depth is greater than HAND
Flood
HAND
Normal

9. Statewide Addresses with HAND
Texas Hand Map (7,600,000 Adress Points)
237 counties mapped
16 CSEC counties geocoded
1 non-CSEC county geocoded
Statewide Addresses with HAND

10. Hand Value 15ft Contour Maps

11. 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

12. 4. Flooding Funding Oppurtunities
TWDB funded 17 projects most of which develop flood early warning systems, but there are other funding sources.
NSF and other funding agencies provide funding opportunities for flooding related research; funding amounts range from $100,000 to $1,500,000.
Development activities, such as those described earlier, can be funded and the City of Austin Flood Early Warning System and the Austin Fire Department could participate as stakeholders in these projects, and hopefully benefit from project results.
Proposals usually require a social scientist to be on the proposal to investigate social, behavioral or HCI aspects of the product developed in this research.
For example, on Feb. 16, 2017 Dr. Eick and S. Camille Perez (Texas A&M University) submitted NSF Planning proposal for $100,000 titled “Design of a Generic Water Level Prediction and Flood Early Warning System” to NSF
UH-DAIS

13. 5. NSF Planning Proposal
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

14. 6. Austin Fire Flood Risk Maps