Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Avaluació de models basats en intel·ligència artificial per a la predicció espacial del risc d'incendi forestal

L'objectiu d'aquest treball final de màster és l'obtenció de models de predicció espacial del risc d'incendi forestal mitjançant l'ús combinat d'intel·ligència artificial, sistemes d'informació geogràfica i big data. S'han utilitzat diversos conjunts de dades d'incendis, meteorològiques, orogràfiques i de vegetació per tal de predir les zones de risc d'incendi i estimar-ne la probabilitat mitjançant tècniques d'aprenentatge automàtic: classificació per a estimar el risc d'incendi forestal, regressió per a predir la mida dels incendis en el moment d'ignició i agrupament per a obtenir zones de risc d'incendi segons les condicions meteorològiques. S'han obtingut models robustos amb precisions de fins al 90% en la predicció del risc i del 99% en l'agrupament de nous exemples en categories de risc en funció de les condicions meteorològiques. Els millors resultats s'han assolit amb l'ús d'aprenentatge profund. Concretament, s'han utilitzat algorismes genètics per tal d'optimitzar l'arquitectura d'un perceptró multicapa. En darrer lloc, els resultats del projecte permeten obtenir mapes de risc amb prou detall per a diversos àmbits (comarques, municipis, espais naturals, etc.) i vàlids per àrees concretes com ara un parc natural on els resultats assolits han permès estimar el risc d'incendi per a les diverses zones del parc i, fins i tot, en determinats indrets sensibles com, per exemple, els principals senders i les zones d'estacionament de vehicles.El objetivo de este trabajo final de master es la obtención de modelos de predicción espacial del riesgo de incendio forestal mediante el uso combinado de inteligencia artificial, sistemas de información geográfica y big data. Se han usado varios conjuntos de datos de incendios, meteorológicos, orográficos y de vegetación para predecir las zonas de riesgo de incendio y estimar la probabilidad mediante diversas técnicas de aprendizaje automático: clasificación para prever el riesgo de incendio forestal, regresión para predecir el tamaño de los incendios en el momento de ignición y agrupamiento para obtener zonas de riesgo de incendio según las condiciones meteorológicas. Se han obtenido modelos robustos con precisiones de hasta el 90% en la predicción el riesgo y del 99% en el agrupamiento de nuevos ejemplos en categorías de riesgo en función de las condiciones meteorológicas. Los mejores resultados se han logrado con el uso de aprendizaje profundo. Concretamente, se han utilizado algoritmos genéticos para optimizar la arquitectura de un perceptrón multicapa. Por último, los resultados del proyecto permiten obtener mapas de riesgo con suficiente detalle para varios ámbitos (comarcas, municipios, espacios naturales, etc.) e incluso válidos para áreas concretas como un parque natural donde los resultados alcanzados han permitido estimar el riesgo de incendio para las diversas zonas del parque e incluso en determinados lugares sensibles como, por ejemplo, los principales senderos y las zonas de estacionamiento de vehículos.The goal of this final master's project is to obtain spatial prediction models of forest fire risk through the combined use of artificial intelligence, geographic information systems and big data. Several sets of fire, meteorological, orographic and vegetation data have been used to predict fire risk areas and to estimate the probability by means of several machine learning techniques: classification to predict the risk of forest fire, regression to predict the size of the fires at the moment of ignition and clustering to obtain fire risk areas according to the meteorological conditions. Robust models have been obtained with accuracies of up to 90% in predicting risk and 99% in grouping new examples into weather-dependent risk categories. The best results have been obtained with the use of deep learning. Specifically, genetic algorithms have been used to optimize the architecture of a multilayer perceptron. Finally, the results of the project allow us to obtain risk maps with sufficient detail for various areas (counties, municipalities, natural spaces, etc.) and valid for specific areas such as a natural park where the results achieved have allowed us to estimate the wildfire risk for the various areas of the park and even in certain sensitive places such as the main paths and vehicle parking areas

Advances in Deep Learning Towards Fire Emergency Application : Novel Architectures, Techniques and Applications of Neural Networks

Paper IV is not published yet.With respect to copyright paper IV and paper VI was excluded from the dissertation.Deep Learning has been successfully used in various applications, and recently, there has been an increasing interest in applying deep learning in emergency management. However, there are still many significant challenges that limit the use of deep learning in the latter application domain. In this thesis, we address some of these challenges and propose novel deep learning methods and architectures. The challenges we address fall in these three areas of emergency management: Detection of the emergency (fire), Analysis of the situation without human intervention and finally Evacuation Planning. In this thesis, we have used computer vision tasks of image classification and semantic segmentation, as well as sound recognition, for detection and analysis. For evacuation planning, we have used deep reinforcement learning.publishedVersio