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Fuzzy Based Landslide Predictionusing Wireless Sensor Networks

Mohamed Shahim P.I.

Guide: Prof. U.B. DesaiSpann-Lab, IIT Bombay

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Introduction Methods for modeling Uncertainties

- Probability

- Fuzzy Logic

- Fuzzy Inference systems

Advantages of Fuzzy:

- Easy to model

- More easy to change the behavior of the system,by changing the rules.

- Less computations compared to hypothesis

testing methods

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Fuzzy logic Fuzzy Sets:

Fuzzy operaters.

Eg: AND -

Fuzzy if-then rules

if (i/p1 is X) then (o/p1 is Z)

( ) min( ( ), ( ))A B A Bx x x

antecedent consequent

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Fuzzy Inference System

Fuzzification: Real values of the input into membershipvalues in appropriate Linguistic variables.

Rule base evaluation: Each of the rules is evaluated to

get an output modified fuzzy set Aggregation: All the output fuzzy sets are combined to

form a single set using max rule

Defuzzification: Combined output fuzzy set is convertedto a number. E.g.. Centroid calculation

Fuzzifier RuleEvaluator

Aggregators De-Fuzzifier

I/P O/P

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FIS information flow

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Fuzzy system model In our case, input is strain values from the sensors

It is divided into 3 linguistic variables as shown below

Output Hazard is also divided into 3 variables

Strain (input)

Low

Medium

High

Hazard-Output

Low

Medium

High

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Input membership functions

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Stress-Strain Characteristics

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Output membership functions

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Rule base

Strain1 Strain2 Strain3 Strain4 Hazard

LOW LOW LOW LOW LOW

LOW LOW LOW HIGH MEDIUM

LOW LOW MEDIUM MEDIUM MEDIUM

.. .. .. .. ..

HIGH HIGH HIGH HIGH HIGH

A total of 81 rules for 4 node setup

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Simulations: Setup consists of 4 nodes and 1 Base Station (BS).

At the BS, FIS calculates output hazard level.

Simulated using MATLAB Fuzzy Toolbox

Strain1 Strain2 Strain3 Strain4 Hazard

0.0 0.0 0.0 0.0 1.7766(low)

0.0 0.0 0.0 1.5 5.0(Medium)

0.0 0.0 0.9 0.9 4.85 (Medium)

.. .. .. .. ..

1.5 1.5 1.5 1.5 8.2234(High)

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ROC: ROC gives the noise performance of the system

The data generated using the VMGP model is used asinput. Output hazard varies from 0 to 10. It isconverted to 2 outputs, Landslide and NoLandslide, using a simple threshold.

The threshold is varied to plot the entire ROC.

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(Input Strain + Noise) vs Time Output Hazard vs Time

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ROC: comparing Fuzzy & CVBD for SNRs

10 dB, 20 dB, 30 dB.

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Previous system uses space correlation, as can beseen in the rules.

Use of time correlation also helps in removingwrong predictions.

One simple method is to remove outliers in outputHazard by averaging.1

0

1( ) ( )

k

i

Hazard t Hazard t ik

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ROC after Averaging the o/p Hazard

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Issues:

The region will be already in a stressed state. A strain gaugecan only measure further change. Difficult to calculate whetherrock has reached 70% of breaking stress.

Heterogeneity of rocks in the Landslide prone region.- This makes fixing of the threshold a difficult task.

Performance variation of strain gauges with respect totemperature.

- Use of Temperature compensation circuits for interfacing

Effect of Orientation of strain gauge on measured stress.- Strain Gauge Rosette: measures greatest strain at a Point.

Noise in Measured Strain

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Future Work:

Use of FLAC3dsoftware for simulating a slopefailure

Extending a single cluster to a bigger network toform a Distributed sensor network

Safety Factor

Distance between motes can be used for detectionmovement in a slope. Helps in detecting rockmovement

Distance can be measured using Received power(RSSI).

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References

Wang Y.,T.Y. Yu and D. Andra, Tornado detection using aneuro-fuzzy method,( Albuquerque), 32ndConference on RadarMeteorology, NM. American Meteorology Society, October 2005

T. Ross, Fuzzy logic with Engineering Applications. Hightstown,NJ :McGraw-Hill, 1995

The Math works Inc. Fuzzy Logic Toolbox Users Guide

H.B. Wang and R. Xu, Slope stability evaluation using backpropagation neural networks, Engineering Geology, vol.80, pp.

302-315, June 2005. FLAC3d Home Page, 2002.

http://www.itascacg.com/flac3d.html