Forest fires are among the most dangerous natural threats that bring calamities to a community and can turn it totally upside down.In this paper, to enable a prevention mechanism, we rely on analytics to build a novel fire danger index model that predicts the risk of a developing fire in north Lebanon. We use correlation methods such as statistical regression, Pearson, Spearman and Kendall's Tau correlation to identify the most affecting parameters on fire ignition during the last six years in north Lebanon.The correlations of these attributes with fire occurrence are studied in order to develop the fire danger index. The strongly correlated attributes are then derived. We rely on linear regression to model the fire index as function of a reduced set of weather parameters that are easy to measure.This is critical as it facilitates the application of such prevention models in developing countries like Lebanon. The outcomes resulting from validation tests of the proposed index show high performance in the Lebanese regions. An assessment versus common widespread weather models is then made and has showed the significance the selected parameters.It is strongly believed that this index will help improve the ability of fire prevention measures in the Mediterranean basin area.

Dermatofibrosarcoma protuberans (DFSP) is a rare neoplasm which represents <0.1% of all tumors but it is considered the most common skin sarcoma. It is a slow-growing tumor that arises from the dermis and invades deeper tissues. The precise origin of DFSP is not well known. It is most frequently seen on the trunk, extremities, and head and neck. The standard treatment of the localized huge DFSP consists of a wide local surgical resection with recommended surgical margins of 2–3 cm. Local recurrence after incomplete excision is common. We present a case of 35-year-old man with enormous bulky mass on the face. Upon histological examination, the diagnosis of DFSP was made, and the patient underwent en bloc wide local excision of the mass followed by the use of Trapezius musculocutaneous pedicle flap reconstruction. On 32 months follow-up, no recurrence has been reported.

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Lebanon is known as a tourist destination for its scenic green mountains but the fires have been threatening this green forestry all over the world. The consequences of forest fires are disastrous on the natural environment and ecological systems, not to mention the population, by worsening poverty and lowering the quality of life. Two data mining techniques are used for the purpose of prediction and decision-making: Decision trees and back propagation forward neural networks. Four meteorological attributes are utilized: temperature, relative humidity, wind speed and daily precipitation. The obtained tree drawn from applying the first algorithm could classify these attributes from the most significant to the least significant and better foretell fire incidences. Adopting neural networks with different training algorithms shows that networks with 2 inputs only (temperature and relative humidity) retrieve better results than 4-inputs networks with less mean squared error. Feed forward and Cascade forward networks are under scope, with the use of different training algorithms.

At the beginning of 20 th century, scientists started to develop mathematical models in order to predict the probability of occurrence of forest fires. Meteorological parameters, such as daily temperature and humidity, were mainly used. In this paper, we review the seven most usable fire prediction indices in the world, that are Angstrom, Keetch-Byram, Modified Keetch-Byram, Canadian fire weather index, Nesterov, Modified Netserov, and Baumgartner Index. A comparative study including the mathematical equations, properties, characteristics, performanceand field of application of each model is presented. The different developed models were optimized to the local characteristics of the place of study. The problematic of suitability and compliance of indices in other regions with different conditions is discussed. Recent initiatives are finally presented. Keywords-forest fire prediction; fire weather indices; limitations and transferabilityI.