Abstract:
Natural disaster, especially cyclone, is a perennial problem for Bangladesh. Every
year cyclones cause colossal damage to the people and economy of the country.
With the increase of environmental deb'fadation, it is expected to increase in the
future. These natural disasters can not be prevented from occurring. Rather, with
proper warning and precautionary measures, the casualties and damages caused
by the cyclones can be reduced greatly.
Information about path and strength of the cyclone can be obtained from
meteorological departments. Using this information, the casualties and damages
caused by cyclone can be assessed through forecasting models. By assessing the
damages, the relief and rehabilitation measures can be planned judiciously. In this
study several models have been developed to forecast the damages caused by
cyclones 'Neural Network' technique has been found to be most applicable in
this case. Data has been collected from various sources such as CARE
Bangladesh Ltd., Cyclone Preparedness Program of Bangladesh Red Crescent
Society and Bangladesh Bureau of Statistics. The variables considered in the
modelling are wind speed of cyclone, height of water surge, distance from the
path of cyclone, population, amount of livestock, numbers of permanent and
temporary houses and length of paved and unpaved roads. Back-propagation
technique has been used for calibration purpose. The modelling approach has been found to be very successful. The model
predictions are highly convergent with the observed results. It has been observed
that the height of water surge has the most detrimental elTect on the casualties.
The neural network technique has been extended further to analyze the effect of
noise in data using 'Monte-Carlo Simulation'. It is observed that the prediction
remains fairly consistent for noise upto twenty percent above which the
predictions become quite inconsistent.
The models developed in this study has a wide range of applications in disaster
management planning, infrastructure planning and, relief and rehabilitation
planning. The approach can be extended further to include other variables related
to cyclone.
