Earthquake Damage Analysis for the Area of Pickering, Ontario, CanadaA.J. McConville | Atef Hamdan | Amir Louaar

McMaster University, Earth Science 3GI3

Earthquakes are one of the most destructive forces in the world1.

They are extremely difficult to predict and it is impossible to prevent

them from occurring3. Earthquakes occur when there is a sudden

movement of the Earth’s crust, caused by the release of stress that

is built up along geologic faults1. One such fault is located virtually

underneath the City of Pickering, Ontario (just off Pickering’s

southern coast in Lake Ontario) and has the potential to cause

enormous destruction to the area and its inhabitants1. The purpose

of this project is to determine the varying levels of destruction in the

City of Pickering if an earthquake were to occur, and where these

damaged areas will be, using Multi-Criteria Evaluation techniques,

GIS, and many specific attribute layers. This project will primarily be

interested in the areas of maximum hypothetical destruction in order

to minimize potential damage in reality and also act as the first step

to proper preparation for an actual earthquake in this area.

The study area is the City of

Pickering, which is located

within the Durham Region of

Southern Ontario, Canada. The

City of Pickering is adjacent to

Lake Ontario, and is also near

the geologic fault that runs

underneath Lake Ontario to the

south. This fault is extremely

close to the shores of

Pickering, as well as the

Pickering Nuclear Power

Plant1.

There are improvements that can be made to make this model and

its results more accurate. For example, soil amplification data was

not available. This data would have enhanced the model by

providing information on how much the underlying soil would shake

or move during an earthquake. There was also very little data

available for the building material used in Pickering. Lastly, a few of

the datasets were incomplete and the missing data had to be

inferred (such as soil type and slope), which compromised the

accuracy of the model in some areas.

When the maps were generated, the City of Pickering was divided

into four regions of varying potential damage that would be caused

by an earthquake. The maximum level of destruction (i.e. red areas)

was located in the southern region of Pickering, close to the

shoreline. This is because the nuclear power plant is in this area

and it is also close to the fault line. The earthquake would have the

least affect on the northern region of Pickering (i.e. green areas) as

it is far from the fault and the power plant , both of which cause the

most damage. In between the maximum and minimum regions are

the areas that would face moderate damage. These areas are

smaller and face lesser destruction due to other factors such as low

soil stability and land use. This is because areas with tall and

congested buildings experience higher levels of destruction than

rural areas due to falling debris.

In conclusion, this map indicates the areas most vulnerable to

earthquake damage. Therefore, Pickering should prepare for an

earthquake and formulate emergency plans according to the

proposed map. Special measures should be taken for areas located

in the southern Pickering. A separate disaster management plan

should also be drafted, with the Pickering nuclear power plant as

the main concern, due to its own close proximity to the fault line. A

meltdown or explosion at this plant would cause catastrophic

destruction, if an earthquake were to occur.

INTRODUCTION AND PURPOSE

STUDY AREA

CRITERIA AND FACTOR WEIGHTING

RESULTS AND RECOMMENDATIONS

MODEL IMPROVEMENTS

METHODS

1) N. Eyles and A. Mohajer (2003, September 22). Discussion of “Analysis and

reinterpretation of deformation features in Ontario”.

<http://cjes.geoscienceworld.org/content/40/9/1299.full>

2) Deluca, p. (2013, October 21). Lecture Module 2: Modeling with Rasters IV. GEO

3GI3. Geography. Avenue to Learn.

https://avenue.cllmcmaster.ca/d2l/le/content/111917/viewContent/932925/View

3) Rauch, Alan F. (2013, November 6). Analysis of Soil Borings for Liquefaction

Resistance.

<http://scholar.lib.vt.edu/theses/available/etd21918229741411/unrestricted/Chp>.

REFERENCES

SENSITIVITY ANALYSIS

DATA SOURCES

DMTI Spatial Inc, Industrial and Resource (IRP) edition: 2010.3.

DMTI Spatial Inc, Municipal Boundary – Lower and Single Tier (MUNICLOQ) edition: -.

DMTI Spatial Inc, Digital Elevation Model (DEM) – Provincial Tiled Dataset, edition: v2.0

DMTI Spatial Inc, Bedrock Geology of Ontario, Fault – 1: 250 000, edition: -.

DMTI Spatial Inc, Land Use (LUR) edition: 2010.3.

DMTI Spatial Inc, Soil Survey Complex, edition: v4.0.

DMTI Spatial Inc, Ontario Hydro Network (OHN) – Waterbody, edition: v1.1.

DMTI Spatial Inc, Major Roads and Highways (HRD) edition: 2011.3.

Figure 2: A map of Pickering, Ontario, showing the levels of

destruction using the pair-wise comparison method.

Figure 3: A map of Pickering, Ontario, depicting the levels of

destruction using the rank-reciprocal comparison method.

Figure 4: A map of Pickering, Ontario, depicting the levels

of destruction using the point allocation comparison method.

Figure 1: A map of the Province of Ontario with a

zoomed in image of the City of Pickering.

Criterion Classification

Straight

Ranking

(Importance)

Constraints and Factors

Municipality Constraint N/AWithin the Pickering

Municipality

Power Plant

ProximityFactor 1

Areas closer to the Pickering

Power Plant

Fault

ProximityFactor 2 Areas closer to the fault

Soil Type Factor 3 Areas with less stable soils

Slope

(Elevation)Factor 4 Areas with steeper slopes

Land use Factor 5 Dense urban areas

Before making the map, some key data assumptions were made, such as slope value

interpolation, roughly defining soil boundaries, and limiting the destruction to Pickering.

The Maximum Score Procedure (Benefit Criteria), which converts raw data into

standardized scores, was used with the Pair-Wise Comparison Weighting Scheme to

find areas of maximum destruction2. The Maximum Score Procedure is useful since it

shows linear proportions in the data, and maintains the relative order of magnitude for

the scores, while maximizing important criteria2. In Pair-Wise Comparison, each factor

is compared to every other criteria until all pairs have been used, and each criterion is

compared against all others to determine its importance. This is advantageous, since

it ranks all criteria simultaneously2. The determined weights, criterion layers, and

binary constraints were all multiplied together to find the areas of most destruction.

The sensitivity analysis compared the Rank Reciprocal, Point Allocation, and Pair-

Wise Comparison methods to find the sensitivity of the weights when altered.

Point Allocation is a user-influenced rating method, while Rank Reciprocal is a

simple ranking method for criteria using a preference order by calculating

reciprocals2. The Sensitivity Analysis showed that the weights were somewhat

sensitive, but mostly quite strong. The Point Allocation map showed sparse,

unrealistic maximum damage areas while the Rank Reciprocal method similar to

the Pair-Wise method, but had less detail on destruction level boundaries and

mostly smoothed them out. The Pair-Wise map showed maximum damage areas

most accurately and was therefore chosen as the final map.

The following criterion factors were ranked according to

importance in the model, with 1 representing the most important

factor and 5 representing the least important factor.