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    Öğe
    A comparative Bayesian optimization-based machine learning and artificial neural networks approach for burned area prediction in forest fires: an application in Turkey
    (Springer, 2023) Yazici, Kubra; Taskin, Alev
    This study presents a prediction methodology to assist in designing an effective resource planning for wildfire fighting. The presented methodology uses artificial neural networks, bagging and boosting in ensemble learning algorithms, and traditional machine learning algorithms decision tree regression, Gaussian process regression and support vector regression to determine the size of the area to be burned in a forest fire that will start. The Bayesian optimization algorithm, which is used in the learning process of the methods, provides the optimum hyperparameter values of the methods to obtain the minimum error value. The methodology, which is first used to predict the size of the fires that occurred in different regions of Turkey between 2015 and 2019, yielded successful results. Second, it is applied to the Montesinho Natural Park forest fire dataset in Portugal to validate its robustness in different geographical regions. Finally, the results are compared with different studies in the literature. Compared with the literature, it is seen that the presented methodology has high accuracy and high speed in the prediction of the burned area. The results of the study are significant as the proposed methodology provides valuable information to the authorized units regarding resource planning during the forest fire response phase. Furthermore, the findings show that the presented methodology is reliable and can be used as an additional tool to predict the burned area for different countries.
  • [ X ]
    Öğe
    Perspective on secondary disasters: a literature review for future research
    (Springer, 2024) Sahin, Kuebra Yazici; Kavus, Bahar Yalcin; Taskin, Alev
    Secondary disasters are catastrophic events that cause social and economic damage triggered by primary disasters. The need to reduce the increasingly destructive impact combined with primary disasters attracts researchers despite the difficulties of complex relationships between disasters and uncertain parameters. This study analyses 92 studies on applications in the field of secondary disaster, consisting of articles and books published in various journals and conference proceedings from 2010 to 2024. The existing literature is categorized according to the types of disasters and methodologies used to highlight trends and gaps in the secondary disaster field. In addition, the relationships between disasters and the diversity and limitations of the methodologies used are also included in this study. The findings of the study reveal the following: (i) post-earthquake landslide disaster applications dominate the secondary disaster field, while other disasters, such as geophysical, meteorological, biological, etc., have not been sufficiently analyzed, (ii) applications for secondary disasters are largely based on susceptibility modeling, (iii) artificial intelligence-based and probabilistic models dominate applications in the existing literature. Eventually, various possible future research paths are provided that may be valuable to decision-makers in decreasing catastrophe loss and damage.