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arXiv 2023年

作者： Palacios-Meneses, David Carrasco, Jaime Dávila-Gálvez, Sebastián Martínez, Maximiliano Mahaluf, Rodrigo Weintraub, Andrés Industrial Engineering Department University of Chile Santiago Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Universidad de Santiago de Chile Santiago Chile Complex Engineering System Institute - ISCI Santiago Chile

The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ignition to extinction. Thus, the placement of firebreaks can be considered a stochastic optimization problem where: (1) the objective function is to minimize the expected cells burnt of the landscape;(2) the decision variables being the location of firebreaks;and (3) the random variable being the spatial propagation/behavior of fires. In this paper, we propose a solution approach for the problem from the perspective of simulation-based optimization (SbO), where the objective function is not available (a black-box function), but can be computed (and/or approximated) by wildfire simulations. For this purpose, Genetic Algorithm and GRASP are implemented. The final implementation yielded favorable results for the Genetic Algorithm, demonstrating strong performance in scenarios with medium to high operational capacity, as well as medium levels of stochasticity. © 2023, CC BY-NC-SA.

关键词： Genetic algorithms

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Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement

arXiv

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arXiv 2024年

作者： Murray, Lucas Castillo, Tatiana Carrasco, Jaime Weintraub, Andrés Weber, Richard de Diego, Isaac Martín González, José Ramón García-Gonzalo, Jordi University of Chile Industrial Engineering Department Santiago Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Complex Engineering System Institute - ISCI Santiago Chile Data Science Lab. Rey Juan Carlos University Madrid Spain Centre de Ciència i Tecnologia Forestal de Catalunya Solsona Spain

Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, including Mixed Integer Programming, Stochastic Optimization, and Network Theory, have proven effective but are hindered by computational demands, limiting their applicability. In response to this challenge, we propose using artificial intelligence techniques, specifically Deep Reinforcement Learning, to address the complex problem of firebreak placement in the landscape. We employ value-function based approaches like Deep Q-Learning, Double Deep Q-Learning, and Dueling Double Deep Q-Learning. Utilizing the Cell2Fire fire spread simulator combined with Convolutional Neural Networks, we have successfully implemented a computational agent capable of learning firebreak locations within a forest environment, achieving good results. Furthermore, we incorporate a pre-training loop, initially teaching our agent to mimic a heuristic-based algorithm and observe that it consistently exceeds the performance of these solutions. Our findings underscore the immense potential of Deep Reinforcement Learning for operational research challenges, especially in fire prevention. Our approach demonstrates convergence with highly favorable results in problem instances as large as 40 × 40 cells, marking a significant milestone in applying Reinforcement Learning to this critical issue. To the best of our knowledge, this study represents a pioneering effort in using Reinforcement Learning to address the aforementioned problem, offering promising perspectives in fire prevention and landscape management. © 2024, CC BY.

关键词： Fires

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Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery

arXiv

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arXiv 2023年

作者： Mancilla-Wulff, Ian Carrasco, Jaime Pais, Cristobal Miranda, Alejandro Weintraub, Andrés Industrial Engineering Department University of Chile Santiago Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Complex Engineering System Institute - ISCI Santiago Chile IEOR Department University of California Berkeley Berkeley United States Universidad de Chile Santiago Chile Departamento de Ciencias Forestales Laboratorio de Ecología del Paisaje y Conservación Universidad de La Frontera Temuco Chile

Monitoring wildfires is an essential step in minimizing their impact on the planet, understanding the many negative environmental, economic, and social consequences. Recent advances in remote sensing technology combined with the increasing application of artificial intelligence methods have improved real-time, high-resolution fire monitoring. This study explores two proposed approaches based on the U-Net model for automating and optimizing the burned-area mapping process. Denoted 128 and AllSizes (AS), they are trained on datasets with a different class balance by cropping input images to different sizes. They are then applied to Landsat imagery and time-series data from two fire-prone regions in Chile. The results obtained after enhancement of model performance by hyperparameter optimization demonstrate the effectiveness of both approaches. Tests based on 195 representative images of the study area show that increasing dataset balance using the AS model yields better performance. More specifically, AS exhibited a Dice Coefficient (DC) of 0.93, an Omission Error (OE) of 0.086, and a Commission Error (CE) of 0.045, while the 128 model achieved a DC of 0.86, an OE of 0.12, and a CE of 0.12. These findings should provide a basis for further development of scalable automatic burned-area mapping tools. © 2023, CC BY-NC-ND.

关键词： Convolutional neural networks

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C2F K: An Open-Source Wildfire Simulator Based on Cell2Fire and the Chilean KITRAL system

SSRN

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SSRN 2023年

作者： Carrasco, Jaime Pais, Cristobal Soto, Felipe Palacios, David Mahaluf, Rodrigo de la Barra, Felipe Gilabert, Horacio Alfaro, Gabriela Miranda, Alejandro Castillo, Miguel Weintraub, Andrés University of Chile Industrial Engineering Department Santiago Chile Complex Engineering System Institute - ISCI Santiago Chile Forest Fire Laboratory University of Chile Box 9206 Santiago Chile Laboratorio de Ecología del Paisaje y Conservación Departamento de Ciencias Forestales Universidad de La Frontera Temuco Chile University of California Berkeley IEOR Department Berkeley United States Santiago Chile Centro de Cambio Global Pontifícia Universidad Católica de Chile Santiago Chile

This paper presents an open-source computational tool that simulates the growth of fires which may occur in any region of Chile, and is also capable of estimating burn probability maps through an iterative and concatenated process of simulating spatially explicit fire ignition and fire growth scenarios. This tool, known as C2F+K, is based on the Cell2Fire simulator and KITRAL, the Chilean fire behaviour system. It incorporates new equations for the calculation of the rate of spread and the length-to-breadth ratio as a function of wind speed that are based on an elliptical shape model. C2F+K also includes a model of crown fire behaviour that is validated with numerical experiments. The system's performance is evaluated through comparisons with real and simulated wildfires using the KITRAL software. C2F+K features cross-platform compatibility and flexibility, allowing it to take advantage of parallel computing for fire growth modeling and the generation of risk maps. © 2023, The Authors. All rights reserved.

关键词： Open source software

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Species Distribution Models Overfitting Effect on Hidden Animal Diversity Estimation: Implications for Conservation Decision-Making

SSRN

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SSRN 2024年

作者： Matus-Olivares, Camilo Carrasco, Jaime Yela, José Luis Meli, Paula Weintraub, Andrés Gil, Fulgencio Lisón Wildlife Ecology and Conservation Lab Departamento de Zoología Facultad de Ciencias Naturales y Oceanográficas Universidad de Concepción Casilla 160-C Concepción Chile Doctorado en Ciencias Agroalimentarias y Medioambiente Facultad de Ciencias Agropecuarias y Forestales Universidad de La Frontera Temuco Chile Laboratorio de Ecología del Paisaje y Conservación Departamento de Ciencias Forestales Facultad de Ciencias Agropecuarias y Medioambiente Universidad de La Frontera Box 54-D Temuco Chile Department of Industrial Engineering Universidad de Chile Santiago Chile Complex Engineering System Institute – ISCI Santiago Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Grupo DITEG Facultad de Ciencias Ambientales y Bioquímicas Universidad de Castilla-La Mancha Avda. Carlos III s.n. Campus Real Fábrica de Armas Toledo45005 Spain Laboratorio de Estudios del Antropoceno Facultad de Ciencias Forestales Universidad de Concepción Concepción Chile

The geographical distribution patterns of animal species communities are essential to design and implement ecological applications and conservation measures successfully. However, to know accurately the species distribution is complex mainly due to three factors: 1) undersampled areas, 2) sampling methodologies not always effective, and 3) uncertainty in the true absence. Therefore, conceptually, there exists a "hidden animal diversity" (HAD) that is possible to measure and quantify, especially through species distribution models (SDMs). Here, we analyzed the overfitting effect of different SDM algorithms over the HAD estimates. Specifically, we a) compare and assess the overfitting levels and predictive performance of different SDM algorithms;b) examine the overfitting effect of different SDM algorithms over HAD estimates and;c) create and assess an evaluation method that allows choosing the most suitable SDM algorithm according to a jointly evaluation of metrics of predictive performance and overfitting. Our results showed that those algorithms with high overfitting levels were worse at predicting hidden animal diversity. We found a significative negative relationship between the expected value of HAD and the level of overfitting of an SDM algorithm. This result is reflected in the distribution maps and it becomes an issue in the decision-making process for the conservation policies. Our findings help improve HAD estimation and have applications in conservation and restoration, providing critical issues on how allocating resources in space, taxonomic groups, or functional guilds. Moreover, we provide a flexible evaluation method that considers the overfitting when evaluating an SDM algorithm. © 2024, The Authors. All rights reserved.

关键词： Conservation

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Corrigendum to “From virtue to sin: Is the installation of bat boxes an effective conservation measure or a potential pitfall for vulnerable bat species?” [J. Nature Conserv. 83 (2025) 126775]

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Journal for Nature Conservation 2025年 84卷

作者： Camilo Matus-Olivares Jaime Carrasco Barra Andrés Weintraub Fulgencio Lisón Wildlife Ecology and Conservation Lab Departamento de Zoología Facultad de Ciencias Naturales y Oceanográficas Universidad de Concepción Casilla 160-C Concepción Chile Laboratorio de Ecología del Paisaje y Conservación Departamento de Ciencias Forestales Facultad de Ciencias Agropecuarias y Medioambiente Universidad de La Frontera Box 54-D Temuco Chile Doctorado en Ciencias Agroalimentarias y Medioambiente Facultad de Ciencias Agropecuarias y Forestales Universidad de La Frontera Temuco Chile Departamento de Industria Facultad de Ingeniería Universidad Tecnológica Metropolitana Santiago Chile Complex Engineering System Institute – ISCI Santiago Chile Department of Industrial Engineering Universidad de Chile Santiago Chile

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