Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.

Year 2019, , 26 - 38, 31.08.2019

İrfan Macit

https://doi.org/10.34110/forecasting.484765

Abstract

Earthquake
is one of the natural disaster types that suddenly breaks regular human life.
Rescue activities in disasters are one of the most critical stages of modern
disaster management. This management stage, as mentioned earlier, includes all
the activities that need to be done after the disaster. Search And Rescue (SAR)
teams perform one of these most critical activities after the earthquake
post-disaster period. Search and rescue teams that will rescue and relief after
a disaster are selected according to the criteria selected. Location layout
selection problems are NP-Hard, and obtaining hard results is in the class of
these problems. One of these criteria is the Risk Pressure Factor (RPF) used in
determining the priorities of the risk areas. Determining the level of risk
level is very difficult and also these are difficult to predict. In this study,
it is aimed to estimate this parametric value by using an artificial neural
network (ANN) method which is applied in many fields. And then in this study, a
prediction model was constructed by using back propagation method which is a
suitable propagation method in ANN method and results are obtained from the
MATLAB program. The resulting risk-pressure factor (RPF) value can be used as a
parameter in the proposed mathematical model. As a result of the study, the
missing parameter of the mathematical model will be found in the estimation of
a parameter belonging to the proposed mathematical model.

Keywords

Artificial Neural Network, Mathematical Modelling, Risk Factor Prediction

References

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