group four reseacrh paper
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RESEARCH PAPER
GROUP 4
RESHMINDER KAUR A132843
NOR IZHAM NOHANZI A133921MOHD IMRAN MOHD JUNAIDI A133239
MUHF NURHILMI SAHARIN A134058
AMIEROUL IEFWAT AKASHAH A133697
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Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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TERMINOLOGIES
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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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
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SPATIALPREDICTIONOFGROUNDSUBSIDENCESUSCEPTIBILITY
USINGANARTIFICIALNEURALNETWORK
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
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INTRODUCTION
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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SPATIALPREDICTIONOFGROUNDSUBSIDENCESUSCEPTIBILITY
USINGANARTIFICIALNEURALNETWORK
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.
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SPATIALPREDICTIONOFGROUNDSUBSIDENCESUSCEPTIBILITY
USINGANARTIFICIALNEURALNETWORK
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.
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SPATIALPREDICTIONOFGROUNDSUBSIDENCESUSCEPTIBILITY
USINGANARTIFICIALNEURALNETWORK
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.
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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
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Sadong Group
Bamchi formations
Jangseong formations
Gobangsan Group
Hambaeksan formations
Tosagok formations
Kohan formations
PYEONGAN
SUPERGROUP
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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
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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
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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
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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
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> 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)
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OK
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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,
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METHODOLOGY
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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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
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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
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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.
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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
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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.
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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
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RESULTS
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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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.
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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).
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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
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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.
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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
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DIS USSION
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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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
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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
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UndergroundFacilities
Construction
AbandonedCoal Mines
Soft Soil Inlandfill Area
Corrosion ofLimestone
GroundSubsidence
hi d
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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
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2. RELATIVEENVIRONMENTALFACTORSPLAYEDIMPORTANT
ROLESINPRODUCINGTHEFINALMAPPRODUCTS
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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
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3. PRIMARYVALUEOFRESULTSPROVEDTOBE AROBUSTAND
USEFULLTOOLFORESTIMATINGANDMAPPINGSUBSIDENCEEVEN
WITHSOMEINCOMPLETEDATA
In this study,
Using the ANN,the relative importance and
weights of factor were calculated
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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
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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
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ON LUSION
Spatial Prediction of Ground Subsidence Susceptibility
Using an Artificial Neural Network
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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
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