2,942 research outputs found

    A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes

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    Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks

    Bayesian networks: a probabilistic tool for species distribution models

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    Las redes bayesianas son un modelo probabilístico multivariante que permite trabajar con incertidumbre. En general, su aplicación en los modelos de distribución de especies es escasa y centrada en el tratamiento de datos discretos sin aprovechar toda su potencialidad. En este trabajo se estudian las redes bayesianas como una herramienta para resolver distintos problemas en los modelos de distribución de especies: clasificación, caracterización y regresión. La posibilidad de trabajar con datos discretos y continuos simultáneamente, la variedad de problemas que pueden resolver, y la flexibilidad en la estructura del modelo, convierten a las redes bayesianas en una herramienta apropiada, en los modelos de distribución de especies, en particular y en Macroecología, en general.Bayesian networks are multivariate probabilistic models able to deal with uncertainty. They have been hardly applied in species distribution models, and have mainly focused on discrete variables without taking advantage of their potentiality. In this paper, Bayesian networks are presented as a tool to solve different problems in species distribution models such as classification, characterization and regression. Their ability to deal with discrete and continuous data simultaneously, the variety of problems that can be solved, and the flexibility in the model structure, make them an appropriate tool in species distribution models and Macroecology

    A review of machine learning applications in wildfire science and management

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    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
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