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Some natural disasters like animal
attacks, earthquakes and wildfires are highly unpredictable and hence it is
difficult to develop a prediction mechanism. Yet, the natural disasters
like    tsunami, floods and landslides
trigger either due to an unpredictable earthquake or a heavy rainfall and it
results a possibility of developing early warning systems. Among the
predictable disasters, landslide is a natural disaster that Sri Lanka
experience on and off. In the recent past, landslides were reported more
frequently and some of those were severe ones. Some of the most recent
experiences were the Meeriyabedda landslide that took place in 2014 and
Aranayake Samassara Kanda landslide that took place in 2016. Aranayake
landslide incident is considered as the worst ever landslide in Sri Lanka was
triggered by the bad weather condition and heavy rainfall prevailed in the
country for several days and ultimately it resulted a tragedy by taking away
many lives. Even though these type of natural disasters are inevitable, the
damage from such disasters can certainly be minimized if proper prediction
mechanisms are in place.






Figure 1-3 : Reported deaths in Sri
Lanka from 2000 to 2016 due to landslides 1

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Minimizing the damages cause by
landslides is the main motivation for this project and the Aranayake incident
was the most recent incident which highlighted the need for an accurate
prediction mechanism. Identifying the occasions where there is a high
possibility of occurring a landslide and issuing warnings to the people who
live in the particular area can minimize the number of deaths and the damages
since people can move to a safe area along with their movable properties before
the land sliding starts. 

Landslide, which is the movement of a mass rock, earth or debris down a slope,
occurs due to the imbalance of upward and downward forces on a slope. The balance
of downward forces such as weight of the soil mass and the upward forces such
as friction can be disturbed either by natural causes or undesired human
activities. The heavy rainfall and earthquakes can be identified as the two
major natural causes for landslides whereas making steep cuts on hills,
uncontrolled rock quarrying and blasting, blocking natural waterways, removal
of forest cover or arson, retaining water on upper slopes etcetera are the
undesired human activities which increase the possible threat for landslides.
Rock falling, debris sliding, creeping, rock sliding, debris falling and
rotational sliding are the main types of landslides which have been occurred in
Sri Lanka up to now.

Figure 1-4 : Occurrence of a landslide

In the context of Sri Lanka case,
landslide susceptibility maps are prepared on the basis of model developed in
1995 by a method of collective wisdom of experts considering the occurrence of
landslides in a particular area (Badulla and Nuwara Eliya districts) using six
major factor combinations. The method has been applied since then to many other
parts of the country without in depth consideration to the local situations.
There are several areas in Sri Lanka which are identified as landslide prone
areas. These areas are considered as the areas where there is a high risk of
occurring a landslide. Kalutara, Galle, Hambantota, Nuwara Eliya, Matale,
Kandy, Kegalle, Ratnapura, Matara and Badulla are some of the district where
there is a comparably high risk of landslides.  











Figure 1-5 : Landslide prone areas in
Sri Lanka 1



Table 1-1 : Population
density in landslide prone areas 1








Although the undesired human activities can be easily
identified and controlled with the laws and regulations prevailing in a
country, proactive identification of the landslides which occur as a result of
natural causes is a challenging task. The only possible way of minimizing
the   widespread devastation of a
landslide is the early detection.


So far, many static
and dynamic models based on different approaches have been locally tested,
validated and improved in various parts of the world. However, a numerous
number of problems remain still unsolved hindering accurate prediction of
landslide hazards especially when real time forecasting is in concern. This is
mainly due to the complexity of the landslide processes which involve critical
combinations of extremely heterogeneous causative factors and uncertainty
associated with the prediction of triggering events. The causative factors can 

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