Group Four Reseacrh Paper

8/12/2019 Group Four Reseacrh Paper

1/52

RESEARCH PAPER

GROUP 4

RESHMINDER KAUR A132843

NOR IZHAM NOHANZI A133921MOHD IMRAN MOHD JUNAIDI A133239

MUHF NURHILMI SAHARIN A134058

AMIEROUL IEFWAT AKASHAH A133697


2/52

Spatial Prediction of Ground Subsidence Susceptibility

Using an Artificial Neural Network


3/52

TERMINOLOGIES




4/52

SPATIALPREDICTIONOFGROUNDSUBSIDENCESUSCEPTIBILITY

USINGANARTIFICIALNEURALNETWORK

Subsidence

is the motion of a surface (usually, the Earth's surface) as it shifts

downward relative to a datum such as sea-level.

is the sinking or settling of the ground surface

Ground subsidence

is the settlement of native low density soils, or the caving in of

natural or man-made underground voids.

Spatial data

Data that define a location. These are in the form of graphic

primitives that are usually either points, lines, polygons or pixels.

Vector data

A representation of the world using points, lines, and polygons.Vector models are useful for storing data that has discrete

boundaries, such as country borders, land parcels, and streets.

Susceptibility

The state or fact of being likely or liable to be influenced or harmed

by a particular thing


5/52



Artificial Neural Network (ANN)

are computational models inspired by an animal's central nervous systems (in

particular the brain) which is capable of machine learning as well as patternrecognition.

Geographic Information System (GIS)

is a computer system designed to capture, store, manipulate, analyze, manage,

and present all types of geographical

Digital Elevation Model (DEM)

The representation of continuous elevation values over a topographic surface by a

regular array of z-values, referenced to a common datum. DEMs are typically

used to represent terrain relief.

Rock Mass Rating (RMR)

Is a geomechanical classification system for rocks developed by Z.T. Bieniawski

between 1972-1973


6/52

INTRODUCTION




7/52



In South Korea, the coal industry played an important role in 1960s1970s.

Began to declinein 1980s along with the decreasein international oilprices.

In 2005, only seven of 345 coal mines operating nationwide.

There is NO MEASURES taken to protect against environmentaldamage after the mine closed.

Various heavy metal flow from mine waste heaps in leachate,causing serious pollutionin riversand soil.

Underground subsidence can cause damageto surface structureaswell as human injury.

Ground subsidence is treated by simple reinforcements after thesubsidencehas occurred.


8/52



The objective of this study is:

To assessed and predicted discontinuous

residual subsidence to produce a ground

subsidence susceptibility (GSS) map of anarea near abandoned underground coal

mines using an artificial neural network

(ANN) in a geographic information system(GIS) environment.


9/52


10/52



The present study assessed and predicted GSS using raster

databases, in anArcGIS grid format, of topographic, geologic,and geotechnical data and the locations of subsidence areasalready discovered in the study area.

ArcGIS 9.3 software (ESRI, CA) was used for database

construction, coordinate conversion, grid production, overlayanalysis, and spatial analysis.

Using the major factors, GSS maps were drawn by applicationof ANN models and then validated by area-under-the-curve

analysis and a field survey.

By this approach, the major influences on ground subsidencewere determined in a limited 1-km2 region, and a method forpredicting GSS efficiently was established.


11/52


12/52

Jeong-am (3712037130N, 12853101285410E; see Fig. 2), was an important coalmining area

lies between Mt. Baek-Wu to the southwest and

Mt. Ham-Beak to the southeast

still has many cavities remaining from mining.

Thus, areas of likely ground subsidence exist inJeong-am.

all coal in South Korea is anthracite, 85% - upperPaleozoic era & lower Mesozoic era in theJangseong Formation of the PyeonganSupergroup

STUDY AREA


13/52


14/52

Sadong Group

Bamchi formations

Jangseong formations

Gobangsan Group

Hambaeksan formations

Tosagok formations

Kohan formations

PYEONGAN

SUPERGROUP


15/52

Jangseo

ngFormation

includes several coal beds, -

workable quality and thickness.

Coal mining - occurred

1967 until 1989

Coal seams - have steep slopes(>6070).

Average seam thickness was~1.32.5 m


16/52

JangseongFormation

composedmainly of

alternatingsandstone andshaledeposits, withthe shale

havingintercalationsof two to threecoal bedseams

HambaegsanFormation

an upperstratum of the

major coaldrifts, iscomposed ofcoarselygrained meta-

sandstone andgray shale andis relativelyresistant toweathering

However,thedevelopmen

t ofcleavagesfrom severefolding hasled to

weaknessesin the rockmasses ofthisformation

A l l d ( 38) h


17/52

A local road (no. 38) showsfeatures of typical sinkholecollapse and deformations andcracks in the road surface

Ground subsidence can occur inareas of past underground miningactivity.

In underground mines, groundsubsidence develops from themine roof to the ground surface.

Mine collapse with time isattributable to decreased shearstrength, groundwater injection,

and increased seepage force aftercoal mining


18/52


19/52

depth and heightof mine cavities

excavation method

degree ofinclination of theexcavation

scope of mining,structural geology

flow ofgroundwater

the mechanicalcharacteristicsrepresented by therock-mass rating(RMR)

In this study,locations of groundsubsidence andfactors governing theoccurrence of groundsubsidence werecollected in a vector-

type spatial databaseand then representedon a grid using theArcGIS softwarepackage.

The spatial databaseis listed in Table 1

INPUT FACTOR


20/52

> national organizations, such as the Coal Industry Promotion

Board (for ground subsidence),

> the National Geographic Information Institute (for topography

and land use),

> the Mine Reclamation Corporation (for mine tunnels and

boreholes), and

> the Korea Institute of Geoscience and Mineral Resources (for

geology)


21/52


22/52


23/52

OK


24/52


25/52

Areas of buildings, mountains, railways, fields, rivers,complex area, roads, and multi-purpose area use were

extracted from the land-use map

The slope angle was obtained from the digital elevation map.

a triangulated irregular network was made using theelevations.

Contours (5-m intervals) and survey base points of elevationwere from the topographic map,


26/52

METHODOLOGY




27/52

An ANN is a computational mechanism able to acquire,represent, and compute a mapping from one multivariatespace of information to another, given a set of datarepresenting that mapping

Purpose of an ANN to build a model of the data-generated weighting process so that the network can

generalize and predict output from inputs that is has notpreviously seen

Hidden- and output-layer nodes process their input by

multiplying them by a corresponding weight, summing theproducts, and processing the sum using a nonlineartransfer function


28/52

An ANN learns by adjusting the weights between the

nodes in response to errors between the actual and

target output values

At the end of phase, neural network provides a model

that should be able to predict a target value from a given

input value

Two stages are involved in using neural network for

multisource classification

1) Training stage

2) Classifying stage


29/52


30/52

PROCEDURE

Training sites selected based on scientific and objective

criteria, location considered likely and unlikely to haveGSS were selected as training sites

Areas where ground subsidence has not occurred were

classified as areas not prone to ground subsidence and50% of areas where ground subsidence was known to

have occurred were assigned to the areas prone to

ground subsidence.


31/52

The back-propagation algorithm was then applied to

calculate the weights between the input and hidden

layers and between the hidden and output layers

An 8x16x1 structure was selected for the network, and

input data was normalized in the range of 0.1-0.9

Learning rate was set as 0.01, initial weights were

randomly set as values between 0.1-0.3

The weights calculated from ten test cases werecompared with determine whether the variation in the

final weight depended on the selection of the initial

weight


32/52

Weight between layers acquired by neural network

training were calculated in reverse, and the contribution

or importance of each factor were determined

Weight that represent the contribution or importance of

each factor were determined

MATLAB software used for weight calculation and

interpretation of the weight

The model was trained for 5000 epochs, and the rootmean-square error(RMSE) value used for the stopping

criterion was set at 0.01.


33/52

If this RMSE value was not achieved, then the maximum

number of iterations was terminated at 5000 epochs.

An epochs means the entire training set to the neural network

The maximum RMSE value when the latter case occurred was

0.214. The final weight between layers acquired during training

of the neural network, and the contribution or importance ofeach of the eight factors, were used to predict GSS

Finally, the weights were applied to the entire study area, and

GSS maps were created for each training case


34/52

RESULTS




35/52

WEIGHTDETERMINATIONANDGSS MAPPING

Final weights between layers acquired during training of the neural network and thecontribution or importance of each of the eight factors used to predict GSS.


36/52

The calculations were repeated ten times to allow the results to achieve similar values.

The SD of the results ranged from 0.014 to 0.033.

The average values were calculated, and divided by the average of the weights of the

factor having the minimum value.

Among the weights, distance from lineament had the highest value (1.5491) and

RMRhad the lowest (1.000).


37/52

The GSS values were classified by equal areas and grouped into five classes (% of area) of

GSS rank for easy visual interpretation:

- very high (5%), high (5%), medium (10%), low (20%), and low (60%).

The minimum and maximum values were 0.008 and 0.952.

The mean and SD were 0.205 and 0.221


38/52

VALIDATION

Predictions made using the ANN method were compared with expected

results based on knowledge of the factors.

Rate curves - the calculated GSS values of all grids in the study area were

sorted in descending order which divided into 100 classes in accumulated

1% intervals.

AUC was calculated to compare the results. A total area of 1 denotes perfect

prediction accuracy for all cases.

The AUC method can be used to assess prediction accuracy qualitatively.

The percentages of validated results appear as a line in Fig. 7.


39/52

For case no. 1, the highest accuracy, 10% of the study area having a greater GSS

could explain 90% of all ground subsidence.

20% of the study area where the GSS value had a greater rank could explain 99% of

ground subsidence (Fig. 7).

AUC values for the ANN produced GSS maps were between 0.9484 and 0.9598


40/52

DIS USSION




41/52

HIGHLIGHTS

1. The need to have an overall and systematic analysis methodfor understanding the effects of each factor and interactions

among factors

2. Relative environmental factors played important rolesinproducing the final map products

3. Primary value of results proved to be a robust and usefull

toolfor estimating and mapping subsidence even with someincomplete data


42/52

1. THENEEDTOHAVEANOVERALLAND

SYSTEMATICANALYSISMETHOD

May take a very long time from thebeginning of ground subsidence inunderground cavities till visible damagesoccur at the surface

When an underground mine isabonded,ground subsidence develops fromthe mine activity roof to the ground surface

Various factors can generate groundsubsidence and there is complex relationamong those factors


43/52

UndergroundFacilities

Construction

AbandonedCoal Mines

Soft Soil Inlandfill Area

Corrosion ofLimestone

GroundSubsidence

hi d


44/52

In this Study,

1) GIS techniques used to study the prediction and management

of ground subsidence in abandoned mines

2) An ANN model applied to assess and predict GSH in Jeong-

am,South Korea,a region where ground subsidence is

expected to continue in the future

3) Influences of factors that are expected to affect werequantitavely analysed

4) Maps of GSS were made using ANN and repeated 10x

5) Training Sites extracted from ground-subsidence areas

6) Validation Showed 94.84 and 95.98 % prediction accuracy

(Ave:95.41%)-Similar & Satisfactory

2 RELATIVE ENVIRONMENTAL FACTORS PLAYED IMPORTANT


45/52

2. RELATIVEENVIRONMENTALFACTORSPLAYEDIMPORTANT

ROLESINPRODUCINGTHEFINALMAPPRODUCTS


46/52

RMR had little influence on final model results considering

paucity of data and narrow value variation range for those few

data that were extrapolated

DOES NOT MEAN that RMR is Unimportant Factor in

defining subsidence hazard but available data is insuffiecient to

support any other conclusion

Spatial Distribution of Lineament Data extended across the

entire area,these data are expected to have had greater

influence on model results


47/52

3. PRIMARYVALUEOFRESULTSPROVEDTOBE AROBUSTAND

USEFULLTOOLFORESTIMATINGANDMAPPINGSUBSIDENCEEVEN

WITHSOMEINCOMPLETEDATA

In this study,

Using the ANN,the relative importance and

weights of factor were calculated


48/52

Factor Average Weight

Slope 0.106 1.055

Depth of Drift 0.023 1.303

Distance from Drift 0.027 1.184

Depth of Groundwater 0.021 1.180

RMR 0.014 1.000

Distance from lineament 0.033 1.549

Geology 0.016 1.376

Land Use 0.019 1.360

Weights of Factor


49/52

Slope

10%

Depth ofDrift

13%

Distance

from

Drift

12%

Depth of

Groundw

ater

12%

RMR

10%

Distance

from

lineament15%

Geology

14%

Land Use

14%

Weights of

Hydrogeological factors

in GSH Analysis

The determined weights

indicates geological factors such

as geology and lineament are

important for ground subsidence

compared to others

Slope was not important

RMR data have limitedavailability and low accuracy

,showed low weight


50/52

ON LUSION




51/52

Study have shown factors involved in ground subsidence andthe method and findings can be applied to GSS mapping in

other regions

GSS Map produced can be used to mitigate hazards to peopleand facilities

Locating monitoring and facility sites for establishing plans

to prevent ground hazards


52/52

Documents

Group Four Reseacrh Paper