Analysis of preparedness and recovery strategies for Virginia&amp;#x2019;s Transportation Systems

Gagnon, T.; Kim, D.-W.; Roseberry, M.; Tucker, Scott; Santos, Joost R.; Haimes, Yacov Y.; Barker, Kash; Dickey, Brett D.; Orsi, M.J.

doi:10.1109/sieds.2008.4559689

Cited by 1 publication

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References 3 publications

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“…Disaster rescue and relief period are in crisis period which is very crucial activity. This period includes recovering the survivors from the disaster area [76,77]. Search and rescue teams work together appropriate recover plan [78].…”

Section: Disaster Management and Rescue Reviewmentioning

confidence: 99%

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

Macit¹

2019

Turkish Journal of Forecasting

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A B S T R A C TEarthquake 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 the backpropagation method which is a suitable propagation method in the 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. 27In this study, we propose a model including risk pressure factor and forecast the Search and Rescue (SAR) team location and layout mathematical model with the artificial neural network (ANN) method. The aim of this study predicts the risk pressure factor by means of ANN methods. The proposal mathematical model locates the SAR station in exact time window with planning long term period. Literature ReviewANN method can be classified into four main groups such as time-series, simulation, qualitative and cause-effect methods. ANN has many advantages consider to other forecasting methods. Developing better forecasting approaches to reduce or eliminate computation time and its total cost. The Prediction models are based on historical data acquired by a user, and these data are computed by statistical methods [5]. For instance, some of the research concerned with the prediction of using water budget [6], air pollution data prediction [7], emergency event prediction [8,9], and rainfall trends [10], wind speed behaviour [11][12][13][14], financial models and prediction [15]. The other approach of this survey is classified as strategic location and layout problems as static or deterministic location problems, dynamic location layout problems or location problems under uncertainty [16]. Emergency service, medical plant, and ambulance service point location and layout problem are surveyed by many authors [17][18][19][20][21][22][23][24]. While some surveys cover only location layout, the other relatives from these research area such as disaster [25][26][27]. S...

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Section: Disaster Management and Rescue Reviewmentioning

confidence: 99%

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

Macit¹

2019

Turkish Journal of Forecasting

View full text Add to dashboard Cite

show abstract

Analysis of preparedness and recovery strategies for Virginia’s Transportation Systems

Cited by 1 publication

References 3 publications

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

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

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