Afet yönetiminde tahmin ve optimizasyon uygulamaları

This study considers the preparedness and response phases of a disaster management problem. One of the biggest problems for disaster management is that accurate predictions for the number of death and injured people are not available. Researchers in disaster management usually use data from the historical disasters as if they were the true numbers of death and injured people. However, in a future disaster, the numbers of injured and death people will be different with probability one. Consequently, recent studies focus on predicting these numbers applying different machine learning techniques such as neural networks, support vector machine, support vector regression, Gaussian process, and multiple linear regression. In this study, we use Gaussian processes to predict the numbers of injured people. This prediction method uses only two types of explanatory variables, namely, population density and the number of undamaged, slightly and moderately damaged buildings. We use the data resulting from the two earthquakes occurred on 6 February 2023 in the southeastern region of Turkey, and we collect the data from different sources. We compare our prediction method with three other benchmark prediction methods. None of the methods provide very accurate predictions. However, Gaussian processes have the advantage of estimating the variance of the predictor in addition to the predictor, and the predictor has normal distribution. We further use these predictors and their variances to solve a chance-constrained optimization problem to find shelter locations. We use randomly generated data for the optimization problem. Further research has to be done to provide: i- machine learning method with better prediction capacity; ii- solution of the shelter location problem with real data; iii- solution procedure to solve a large-scale optimization problem for shelter location problem.xii Keywords : Machine learning, Gaussian process, Chance constraints, Shelter location problem, Disaster management