Deep Representation Learning and Prediction for Forest Wildfires

deep learning;lrcn;cnn;autoencoder;neural network;caffe;forest fire;spatially spreading problem

Zohouri Haghian, Pardisadvisor:Crowley, Mark ; affiliation1:Faculty of Engineering ; Crowley, Mark ;

University of Waterloo

关键词: forest fire;    deep learning;    cnn;    spatially spreading problem;    lrcn;    autoencoder;    Master Thesis;    caffe;    neural network;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/14644/3/ZohouriHaghian_Pardis.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it.