1. Presented by: Chung-Hsien Yu
Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction
Department of Computer Science
Presented by: Chung-Hsien Yu
Department of
Civil and Environmental Engineering
2015 IEEE International Conference on Big Data

2. 5-Day Precipitation Forecast
Error Amplification
Heavy Rainfall
Flooding
Source:
2015 IEEE International Conference on Big Data
Paper ID: BigD488

3. Problems and Solutions
Simulation
Data Mining
Short lead-time: Error Amplification
Long lead-time: Source Location Backtracking
Global scale model: Entire spatial features Computationally expensive
Local scale model: Feature reduction Target location focus
2015 IEEE International Conference on Big Data
Paper ID: BigD488

4. Source Location Backtracking
How to find the associations?
Temporal Clusters
Heavy Rainfall
How to extend the lead-time?
Flooding
time series
2015 IEEE International Conference on Big Data
Paper ID: BigD488

5. Spatio-Temporal Asynchronous Co-Occurrence Pattern
Source location A
Asynchronous Co-Occurrence Locations
Lead-time l
Target location
Source location B C
Source location
2015 IEEE International Conference on Big Data
Paper ID: BigD488

6. Asynchronous Co-Occurrence Locations
How to find the asynchronous co-occurrence locations?
length(PWC)
8+3
PWC A
Source location
Lead-time=7
EPC 3
Co-Occurrence Band=3
Target location
Co-Occurrence Rate=3/11
The locations with long PWC…
Asynchronous Co-occurrence Locations:
The locations with over average COR and high COB.
2015 IEEE International Conference on Big Data
Paper ID: BigD488

7. Spatial-Temporal Predictive Modeling
Ensemble model of different lead-time
Asynchronous Co-occurrence Locations
Monitoring the formation progresses
l=4
latitude
longitude
l=5
l=6
2015 IEEE International Conference on Big Data
Paper ID: BigD488

8. The Proposed Framework
Big Climate Data
2015 IEEE International Conference on Big Data
Paper ID: BigD488

9. Case Study: Flood Prediction in Iowa
Avg. time:
15 sec
Number of features:
9  5,328
9  500
Avg. time:
187 sec
Predict the severe floods caused by heavy
precipitation at least 9 days in advance!
Atmospheric factors
Gridded
locations
2015 IEEE International Conference on Big Data
Paper ID: BigD488

10. Summary
Associating precipitation and precipitable water for long-lead flood prediction.
Reducing the training data size for modeling.
Map-Reduce-style parallelization.
Scalable
2015 IEEE International Conference on Big Data
Paper ID: BigD488

11. Acknowledgments Chung-Hsien Yu csyu@cs.umb.edu
Prof. Wei Ding
Dong Luo
Joseph Cohen
Prof. Shafiqul Islam
Prof. David Small
Chung-Hsien Yu
Research Computing Department
at the University of Massachusetts Boston
2015 IEEE International Conference on Big Data
Paper ID: BigD488