Data Verification, Analytics and Visualization

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

3

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





4

Dike failure at Wilnis

Source: European Water Management Online

• 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

5

State of the art: manual dike inspection

6

Intelligent water infrastructure management

Is the technology ready?

7

Enabling technologies: intelligent sensors

• Smart sensors with M2M capability

• Distributed fiber optic sensors

• Measuring strain and temperature over tens of kilometers

• Fiber optic woven into geotextile

• Improved geotechnical properties, combine monitoring and strengthening

Piezometer

Temperature

(to locate water flow / seepage)

Inclinometer

Internet

8

Dike failure mechanisms

Overflow Piping

Heave

Instability of cover

- instability outwards

Instability of foreland

Overflow Piping

Heave

Instability of cover

Marco-instability outwards

Instability of foreland

Instability by infiltration

and erosion by overflow

Marco-instability inwards

Mirco-instability

9

Example dike anomaly monitoring

Risk of piping channel

formation

Risk of uplift

Risk of macrostability slip surface

GeoBeads multi-sensor: measures pore water pressure, inclination and temperature in all relevant soil layers

GeoBeads multi-sensor

Optional GeoBeads inclinometer array for horizontal displacement measurement

GeoDetect

GeoDetect: distributed fiber optic sensors that measure temperature and strain with high spatial resolution

10

Monitoring focus in the breach process

* Figure source: UrbanFlood project

The generic breach outflow hydrograph*

Focus

• T1 – T2: Progression of breach initiation

• T2 – T3: Transition to breach formation

Decision Support

System to predict failure

in near real time

• T0 – T5: Covered by AGT Water Management System

11

Experiments and real-world implementations



• IJkdijk AIO SVT

• Yellow River project

• Livedijk Utrecht project





12

IJkdijk overview

• 2005: IJkdijk Consortium established to explore and test sensor network technologies for early flood warning

• 2008-9: 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

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

14

First dike failure test

• Two test dikes failed on August 26th and 27th 2012

Piping

Micro-stability

20120828 dijkdoorbraak_s.mp4

15

Second dike failure test

• Failure on September 8th 2012

Micro-stability

Macro-stability

16

• Length: 5464 km • Basin: 790,000 km2 • Population: 110 million

• Runoff: 58 billion m3 • Sediment: 1.6 billion ton

Yellow River project location

AGT Yellow River

project location

Facts of the Yellow River

17

Pore Pressure

Dike Inclination Dike Temperature

Water Level

Dike Deformation

Erosion Prevention

Dike Strain

• AGT’s turnkey solution

• Utilizes smart sensors

• Can prediction of failures

• Enables effective maintenance

Yellow River project overview

Visual Picture

Reality Check

18

Yellow River project sensor installation

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

20

Livedijk Utrecht - Grecht Dike

Actual dike

Village of Woerden (50.000 inhabitants)

Grecht canal

Amsterdam 30 kilometers

Focus: monitoring the effects of drought on dikes

21

Livedijk Utrecht Voorhavendijk

Piping

Micro-stability

Macro-stability

Focus: monitoring piping and dike stability

22

3D sensor data visualization







23

3D visualization

Intuitive way to observe sensor data and dike anomaly

24

Dike stability models







25

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

Dyke stability models

26

Calculation

Dike Stability calculation input

http://www.deltaressystems.com/txmpub/images/10559/11070/large/1107.png

27

Cross section

Height of river level

Pore water

pressure

Stability factor

Relative safety

A

low low 1.10 100%

high high 1.07 97%

low high 1.04 94%

1.10 0.94 Safety factor:

85% 100%

Not safe Safe

Dike Stability model output

Relative Safety:

28

Sensor data verification and analytics






• Data Cleansing

• Data-driven prediction

• Model-driven prediction


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?

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.

Data cleansing

30

Data pre-processing: Data cleansing

31

Data Cleansing

Data errors in the form of spikes with temperatures as high as 1000 deg C

•A

fter c

orrecti

on

•B

efo

re C

orrecti

on

32

Approach 1

Data-driven prediction

33

Data-driven anomaly detection

Learning &

Prediction

Water level Temp…

Pore pressure

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

Cle

ansin

g

34

Anomaly detection: Regression to find breakpoint

Linear Regression: y = b X

Pore Pressure

• Water Level • DMC 1 Temp • DMC 2 Temp • DMC 1 Pressure • DMC 2 Pressure

Parameter estimation on training:

with

For correlated data (singular matrix):

Ridge Regression

35

Statistical test

Early anomaly detected

(3 days before dike collapse)

Severe instability detected (2 days

before dike collapse)

36

Approach 2

Model-driven prediction

Healthy dike

Type1 Type2

Failing dike

37

Anomaly detection: Geometrical Model

rg rg

38

Anomaly detection: Geometrical model versus data

Correlation water level versus pore pressure

Installation depth map:

SVT01, -0.72 SVT02, -0.666 SVT03, -0.653 SVT04, -0.699 SVT05, -0.682 SVT06, -0.623

39

Future extension







40

Extension to other water-related areas

Landslide protection and monitoring

Dike overtopping protection and monitoring

41

Extension to Structural Health Monitoring

Photo source: Internet

Thank you

Data Verification, Analytics and Visualization

Documents

Transcript of Data Verification, Analytics and Visualization