Realization of a framework based on decision theory, optimization and diffusion to predict, model and manage a natural disaster : forest fires

Au cours des dernières décennies, le nombre d'occurrences de catastrophes naturelles a augmenté sensiblement. Cette augmentation a des conséquences catastrophiques sur les espaces verts, les propriétés et les êtres vivants. L'énorme quantité de dommages a attiré l'attention des chercheurs, des organisations et des secteurs gouvernementaux et non-gouvernementaux vers l'analyse de ces phénomènes, leurs causes et leurs effets. Le but est de pouvoir reconnaître leurs comportements et prédire leur occurrence pour mieux aborder la phase de gestion des risques qui contribue à empêcher leur incidence ou limiter les conséquences. Le risque de d'incendie existe souvent et la présence de dangers est également possible. D'où l'importance de tout effort pour lutter contre de telles crises. Dans cette contribution, le phénomène des incendies de forêt est étudié. Au Liban, les espaces verts ont considérablement diminué au cours des dernières années, ce qui impose une intervention urgente des politiques et le soutien des organisations gouvernementales et non gouvernementales. L'orientation globale est d’aller vers des techniques qui permettent de prédire les risques élevés d'incendie permettant ainsi de prendre des précautions pour empêcher les occurrences d'incendie ou tout au moins limiter leurs conséquences. La prévision des incendies de forêt contribue à prévenir d'une part la fréquence des incendies et d'autre part, à réduire ses impacts sur les êtres vivants, les propriétés et la richesse forestière....During the last decades, the number of occurrences of natural disasters has increased noticeably which lead to catastrophic results on human as well as properties and green areas. But despite the huge amount of damages, this helps to draw the attention of researchers, organizations and the various governmental and non-governmental sectors towards analyzing these phenomena, their causes & effects, allowing to recognize their behaviors and the methods to predict their occurrence and thus reaching the phase of risk management contributing to prevent their incidence or limit the consequences. As the risk of happening often exists, the instantaneous presence of dangers is also possible. Here appears the importance of any effort that serves to tackle such crises. In this contribution, the phenomenon of forest fires is studied. In Lebanon, green areas declined dramatically during the last decades, what imposes an urgent intervention with strict governmental policies and support of non-governmental organizations. The global orientation is towards techniques that predict high fire risks, allowing for precautions to preclude fire occurrences or at least limit their consequences. Forest fire prediction proves to contribute in preventing fire occurrence or reducing its catastrophic impacts in worst cases on human lives, properties and green forestry...

Comparative Study Between Decision Trees and Neural Networks to Predictfatal Road Accidents in Lebanon

International audienceNowadays, road traffic accidents are one of the leading causes of deaths in this world. It is a complex phenomenon leaving a significant negative impact on human’s life and properties. Classification techniques of data mining are found efficient to deal with such phenomena. After collecting data from Lebanese Internal Security Forces, data are split into training and testing sets using 10-fold cross validation. This paper aims to apply two different algorithms of Decision Trees C4.5 and CART, and various Artificial Neural Networks (MLP) in order to predict the fatality of road accidents in Lebanon. Afterwards, a comparative study is made to find the best performing algorithm. The results have shown that MLP with 2 hidden layers and 42 neurons in each layer is the best algorithm with accuracy rate of prediction (94.6%) and area under curve (AUC 95.71%)

Psychiatric Disorders Comorbid with Epilepsy in A Prison Sample

Purpose: Epilepsy is an extremely widespread and serious neurological disease. Although comorbidities of psychiatric disorders are prevalent in epilepsy patients, quite often this coexistence could be overlooked. Studies in this area demonstrated that depression, anxiety disorders and schizophrenia are the most common psychiatric disorders accompanying epilepsy. Mental health problems are known to be more common in prisoners compared to general population. The present study aims to demonstrate the psychiatric comorbidities in prisoners diagnosed with epilepsy. Method: In this study, demographic data and the psychiatric comorbidity of 200 patients who were diagnosed with epilepsy by a neurologist at Ankara Penal Institution Campus State Hospital between January 2013 and January 2014 were analyzed retrospectively. Results: The mean age of study population was 32.6 +/- 10.1 years. 181 of these patients were male (90.5%). 81 of 200 patients (40.5%) had a comorbid psychiatric disorder. The most common comorbid psychiatric disorders were depression (18.5%), anxiety (11%), and personality disorders (11%), respectively. Conclusion: The most common psychiatric comorbid disorders among prisoners diagnosed with epilepsy were depression and anxiety as general population with epilepsy whereas some disorders, personality disorder, substance dependence and bipolar affective disorders, were found to be more common among prisoners compared to the general population with epilepsy. It is crucial to question psychiatric symptoms and comorbidities while evaluating the patients with epilepsy, especially among prisoners. (C) 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.WoSScopu