Model-Based Monitoring for Early Warning Flood Detection

1. Model-Based Monitoring for Early Warning Flood Detection Elizabeth A. Basha, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Daniela Rus, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology SaiRavela, Earth Atmospheric and Planetary Science Massachusetts Institute of Technology

2. Outline • Motivation • Previous Work • Prediction Model • Sensor Network Architecture • Installation and Results • Conclusion • Pros&Cons

3. Motivation • River flooding detection • Deployment target: rural and developing countries • Requirements: • Withstanding hardware to river flooding and storms • Monitor and communicate over 10000km^2 basin • Predict flooding autonomously • Limit costs allowing feasible implementation in development country

4. Introduction • Flood Prediction Algorithm is based on a regression model. • Nearly as good as that used by hydrology researchers

5. Previous work (1/2) • Sensor network for environmental monitoring • Redwood tree (air temperature, humidity, solar radiation). • Off-line data analysis • Light intensity • Communication via Zigbee • James reserve (humidity, rain, wind) • Deployment in Bangladesh rice paddy to measure nitrate, calcium and phosphate • Volcano • Seismic and acoustic data

6. Previous work (2/2) • None above envision system requirements: • Minimalistic number of sensor available • Real-time need of data • Computational autonomy • Complexity necessary to perform prediction

7. Sensor networks for flood detection • Castillo-Effen • Suggested an architecture but unclear on basin characteristics and no hardware detail • Hughes • Gumstix sensor nodes, linux OS • Tested in the lab but no field test • Planned deployment of 13 nodes along 1km riverside without flood prediction model.

8. Operational systems for flood detection • US Emergency Alert System • Volunteer and limited technology • MIKE 11-based flood forecasting system

9. Computational requirements • SAC-SMA • Modeling different methods of rainfall surface runoff to determine how much water will enter the river • Complex equations to establish the model • Not easily running on sensor network

10. Prediction Model • Rainfall-runoff model: • Computational burden, difficult to customized for individual basin • Statistic model: • Based on observed records • Intrinsically self-calibrated, real-time • Used in other areas such as hurricane intensity forecasting • Linear regression models assume a linear equation can describe system behavior • Weighting the past N records of relevant inputs at time T to produce prediction at T+t • Past prediction errors are allowed

11. Flood prediction algorithm

12. Test data and setup • Use 7 years of rainfall, temperature and river flow data for Blue River in Oklahoma • Compare data to DMPI • 3 criteria for the quality of algorithm: • Modified correlation coefficient • False positive and negative

13. Model Calibration • Training window: 1/3/6/9/12 months • Optimal values of inputs: Sweep the order for each input of past prediction • Pick the best input values with high MCC and low false positive/negative • Other approaches: climatology, persistence • 1/24 hours prediction

14. Sensor network architecture (1/2) • Monitor events over large geographic regions of 10000 km^2 • Provide real-time communication of measurements covering a wide variety of variables contributing to the event occurrence • Survive long-term element exposure, the potential devastating event of interest, and minimal maintenance • Recover from node losses • Minimize costs • Predict the event of interest using a distributed model driven by data collected • Distribute among nodes the significant computation needed for the prediction

15. Sensor network architecture (2/2) • 2-tier communication network • Long-range communication node transmits on the order of 25 km using 144 MHz radio • Low power sensing node operates at 900 MHz • Office and communication nodes for UI

16. Base system • Base system: • ARM7TDMI-S microcontroller core for LPC2148 from NXP • Using photovoltaic charging of lithium-polymer battery at 3.7V

17. Base system hardware • An ARM7TDMI-S microcontroller core • Extend to 8 serial ports by adding Xilinx CoolRunner-II CPDL • Mini-SD card and FRAM supply data and configuration storage • Running software package developed in C using WinARM

18. Communication • AC4790 900MHz modules operate at 76.5 kb/s • Modem uses MX614 Bell 202 integrated circuit

19. Sensing node • Measuring rainfall, air temperature, water pressure • Log data • Compute data statistic over each hour • Analyze data for potential sensor failures

20. Communication node • Computation of prediction • Maintain a record of all values and examine data correction • Request data if encountering prediction model uncertainty

21. Office and community node • Maintained by governmental agencies • Display malfunctioning nodes • Provide data and prediction regarding the event of interests • Community nodes provide a simpler UI and do not supply detailed information regarding node status and private data

22. Installation and results • Test the flood prediction algorithm using a large set of physical river flow data • Demonstrate long-term data collection of river flow data with a sensor network • Test the networking capabilities of 2-tier sensor network in a rural setting

23. Blue River testing • Use a large data set to test prediction algorithm • 7 years of data measured from 1 river flow and 6 rainfall sensors and a weather station • Autocorrelation at 24 hours: 0.627

24. Blue River testing

25. Dover field test • 5 weeks data collection

26. Honduras field tests • Collaboration with FSAR to install the system and understand deployment issues • Radio antennas need line-of-sight high in the air • Possible water measuring system

27. Conclusion • Described a complete architecture for predictive environmental sensor networks over large geographic areas • Nodes-limited and cost constraints • Implementation of flood prediction algorithm and evaluation

28. Pros&Cons • Pros • A complete study • Use off-the-shelf devices • Detailed hardware description • Cons • No real flooding occurred during evaluation • Energy consumption problem