Data Verification, Analytics and Visualization

Transcription

1 Data Verification, Analytics and Visualization Wenhui Zhang, Roel Heremans, Christian Debes, Neha Thakre, Alexander Bauer, Manuel Görtz, Aleksandra Kovacevic The 2nd Asia Pacific Water Summit Technical Workshop Chiang Mai, Thailand, 16 May 2013

2 Agenda Smart sensors in water infrastructure Experiments and real-world implementation 3D sensor data visualization Dike stability models Sensor data verification and analytics Future extension 2

3 Smart sensors in water infrastructure Smart sensors in water infrastructure Need for intelligent water infrastructure management Enabling technologies: intelligent sensors Dike failure types and example monitoring Monitoring focus in the breach process Experiments and real-world implementations 3D sensor data visualization Dike stability models Sensor data verification and analytics Future extension 3

4 Dike failure at Wilnis The dikes at Wilnis in The Netherlands failed in August 2003 Most dike breaches are caused by high water levels. However this failure was due to lengthy drought - dry peat is light and adheres weakly to the base resulting in a dike shifting The effects of drought are hard to observe by visual inspection Source: European Water Management Online 4

5 State of the art: manual dike inspection 5

6 Intelligent water infrastructure management Is the technology ready? 6

7 Enabling technologies: intelligent sensors Smart sensors with M2M capability Piezometer Inclinometer Temperature (to locate water flow / seepage) Internet Distributed fiber optic sensors Measuring strain and temperature over tens of kilometers Fiber optic woven into geotextile Improved geotechnical properties, combine monitoring and strengthening 7

8 Dike failure mechanisms Overflow Instability by infiltration and erosion by overflow Piping Heave Marco-instability inwards Marco-instability - outwards Mirco-instability Instability of cover Instability of foreland 8

9 Example dike anomaly monitoring GeoDetect: distributed fiber optic sensors that measure temperature and strain with high spatial resolution GeoBeads multisensor: measures pore water pressure, inclination and temperature in all relevant soil layers GeoDetect GeoBeads multisensor Optional GeoBeads inclinometer array for horizontal displacement measurement Risk of uplift Risk of piping channel formation Risk of macrostability slip surface 9

10 Monitoring focus in the breach process The generic breach outflow hydrograph* Focus Decision Support System to predict failure in near real time T1 T2: Progression of breach initiation T2 T3: Transition to breach formation T0 T5: Covered by AGT Water Management System * Figure source: UrbanFlood project 10

11 Experiments and real-world implementations Smart sensors in water infrastructure Experiments and real-world implementations IJkdijk AIO SVT Yellow River project Livedijk Utrecht project 3D sensor data visualization Dike stability models Sensor data verification and analytics Future extension 11

12 IJkdijk overview 2005: IJkdijk Consortium established to explore and test sensor network technologies for early flood warning : Large scale field experiments on full scale dikes to test concept and evaluate different sensor technologies Results show that sensor technologies provide valuable addition to regular dike inspection 12

13 IJkdijk All-in-one Sensor Validation Test Objective Study the predictive capability of full-service sensor systems Test preformed Three test dikes (East, West, South dike) were built and brought to failure Multiple potential failure types on one dike - exact failure mechanisms were unknown before failure 13

14 First dike failure test Two test dikes failed on August 26th and 27th 2012 Piping Micro-stability 14

15 Second dike failure test Failure on September 8th 2012 Micro-stability Macro-stability 15

16 Yellow River project location Facts of the Yellow River Length: 5464 km Basin: 790,000 km2 Population: 110 million Runoff: 58 billion m3 Sediment: 1.6 billion ton AGT Yellow River project location 16

17 Yellow River project overview Visual Picture Dike Deformation Reality Check Erosion Prevention Dike Strain Pore Pressure Dike Inclination Water Level Dike Temperature AGT s turnkey solution Utilizes smart sensors Can prediction of failures Enables effective maintenance 17

18 Yellow River project sensor installation 18

19 Livedijk Project in Utrecht Province, Netherlands Customers Provincie Utrecht, Rijkswaterstaat Dienst Utrecht, De Stichtse Rijnlanden Objective Obtain knowledge and experience about sensors in real dikes Locations Grechtdijk Voorhavendijk 19

20 Livedijk Utrecht - Grecht Dike Focus: monitoring the effects of drought on dikes Amsterdam 30 kilometers Grecht canal Actual dike Village of Woerden ( inhabitants) 20

21 Livedijk Utrecht Voorhavendijk Focus: monitoring piping and dike stability Piping Micro-stability Macro-stability 21

22 3D sensor data visualization Smart sensors in water infrastructure Experiments and real-world implementations 3D sensor data visualization Sensor data verification and analytics Smart sensors in water infrastructure Future extension 22

23 3D visualization Intuitive way to observe sensor data and dike anomaly 23

24 Dike stability models Smart sensors in water infrastructure Experiments and real-world implementations 3D sensor data visualization Dike stability models Sensor data verification and analytics Future extension 24

25 Dyke stability models Bishop method (slices) Proven and are based on years of experience Require knowledge of the soil and site survey Only be used in specific situations 25

26 Dike Stability calculation input Calculation 26

27 Dike Stability model output Cross section A Height of river level Pore water pressure Stability factor Relative safety low low % high high % low high % Safety factor: Not safe Safe Relative Safety: 85% 100% 27

28 Sensor data verification and analytics Smart sensors in water infrastructure Experiments and real-world implementations 3D sensor data visualization Dike stability models Sensor data verification and analytics Data Cleansing Data-driven prediction Model-driven prediction Future extension 28

29 Problem statement and approaches Given a heterogeneous sensor setup measuring a variety of physical phenomena related to dike stability: How can we make optimal use of the acquired data to perform early prediction of dike failures? Data cleansing Approach 1 Data-driven prediction Use the available historical sensor data to learn a model for normal behaviour. Test new data against that model and detect anomalies. Approach 2 Model-driven prediction Use an existing theoretical dike model. Test new data against that model and detect anomalies. 29

30 Data pre-processing: Data cleansing 30

31 After correction Before Correction Data Cleansing Data errors in the form of spikes with temperatures as high as 1000 deg C 31

32 Approach 1 Data-driven prediction 32

33 Data-driven anomaly detection Pore pressure Water level Temp Cleansing Learning & Prediction Predicted target variable Statistical Test Measured target variable Anomaly? Some analytics challenges Changing environments Hidden/Unknown dependencies Sensor failure vs. Anomaly Seasonal effects vs. Anomaly and solutions Adaptive learning methods Data driven modelling Advanced sensor data fusion Seasonal models 33

34 Anomaly detection: Regression to find breakpoint Linear Regression: y = b X Pore Pressure Parameter estimation on training: Water Level DMC 1 Temp DMC 2 Temp DMC 1 Pressure DMC 2 Pressure with For correlated data (singular matrix): Ridge Regression 34

35 Statistical test Severe instability detected (2 days before dike collapse) Early anomaly detected (3 days before dike collapse) 35

36 Approach 2 Model-driven prediction Healthy dike Type1 Failing dike Type2 36

37 Anomaly detection: Geometrical Model rg rg 37

38 Anomaly detection: Geometrical model versus data Correlation water level versus pore pressure Installation depth map: SVT01, SVT02, SVT03, SVT04, SVT05, SVT06,

39 Future extension Smart sensors in water infrastructure Experiments and real-world implementations 3D sensor data visualization Dike stability models Sensor data verification and analytics Future extension 39

40 Extension to other water-related areas Dike overtopping protection and monitoring Landslide protection and monitoring 40

41 Extension to Structural Health Monitoring Photo source: Internet 41

42 Thank you