Convolutional Neural Networks for Hurricane Road Closure Probability and Tree Debris Estimation

Hurricanes cause significant property loss every year. A substantial part of that loss is due to the trees destroyed by the wind, which in turn block the roads and produce a large amount of debris. The debris not only can cause damage to nearby properties, but also needs to be cleaned after the hurricane. Neural Networks grown significantly as a field over the last year finding a lot of applications in many disciplines like computer science, medicine, banking, physics, and engineering. In this thesis, a new method is proposed to estimate the tree debris due to high winds using the Convolutional Neural Networks (CNNs). For the purposes of this thesis the tree satellite image dataset was created which then was used to train two networks CNN-I and CNN-II for tree recognition and tree species recognition, respectively. Satellite images were used as the input for the CNNs to recognize the locations and types of the trees that can produce the debris. The tree images selected by CNN were used to approximate the tree parameters that were later used to calculate the tree failure density function often called fragility function (at least one failure in the time period) for each recognized tree. The tree failure density functions were used to compose the probability of road closure due to hurricane winds and overall amount of the tree debris. The proposed approach utilizes the current trends in Neural Networks and is easily applicable, such that can help cities and state authorities to better plan for the adverse consequences of tree failures due to hurricane winds.