1. Data-driven Applications and Decision Making Models in Natural Resources
- Author
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Pais, Cristobal and Pais, Cristobal
- Abstract
The destructive potential of wildfires has been exacerbated by climate change, causing their frequencies and intensities to continuously increase globally. In this context, increasing wildfire activity across the globe has become an urgent issue with enormous ecological and social impacts. Wildfires have consumed important areas and forest resources, as a result, fire management expenditures have increased and thousands of homes and many lives have been lost. Moreover, they have significantly impacted biodiversity and greenhouse gas emissions on a global scale.The current incidents across the globe highlight the need for preemptive policy measures to reduce the risk of fire occurrence, managing the land in an effective way to protect natural forests, agricultural areas, and human lives. These concepts are included in what is known as FireSmart Forest Management (FSFM). This paradigm considers opportunities in three dimensions: i) decrease of the fire behavior potential of the landscape, ii) reduction of the potential for fire ignitions, and iii) increase in the fire suppression capability.This dissertation aims at advancing the theory, practice, and large-scale implementation of complex data-driven decision making and machine learning models in the context of landscape management under wildfire risk, integrating Operations Research, Computer Science, and Data Science techniques. We focus our efforts on the understanding, evaluation, and development of effective prevention and mitigation policies, with the potential of being implemented practice, as well as exploring and developing new FSFM techniques.We divided our study into three main aspects: Simulation, Decision-Making, and Machine Learning. In Chapter 1, we focus on the development and evaluation of an accurate, flexible, and efficient wildfire simulation model that can be integrated with data-driven decision-making models. Empirical results on thousands of simulations show the high performance of the model com
- Published
- 2021