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Artificial Intelligence Applied to Natural Resources Management

Artificial Intelligence Applied to Natural Resources Management

Diana F. Adamatti, Marilton S. de Aguiar
Copyright: © 2012 |Pages: 17
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch604
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MLA

Adamatti, Diana F., and Marilton S. de Aguiar. "Artificial Intelligence Applied to Natural Resources Management." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 1566-1582. https://doi.org/10.4018/978-1-60960-818-7.ch604

APA

Adamatti, D. F. & de Aguiar, M. S. (2012). Artificial Intelligence Applied to Natural Resources Management. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 1566-1582). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch604

Chicago

Adamatti, Diana F., and Marilton S. de Aguiar. "Artificial Intelligence Applied to Natural Resources Management." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 1566-1582. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch604

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Abstract

There are three computational challenges in natural resources management: data management and communication; data analysis; and optimization and control. The authors believe these three challenges can be dealt with Artificial Intelligence (AI) techniques, because they can manage dynamic activities in natural resources. There are several AI techniques such as Genetic Algorithms, Neural Networks, Multi-Agent Systems or Cellular Automata. In this chapter, the authors introduce some applications of Cellular Automata (CA) and Multi-Agent-Based Simulation (MABS) in natural resources management, because these are areas that the authors approach in their research and these areas can contribute to solve the three computational challenges. Specifically, the CA technique can face the challenge of data analysis because it can be extrapolated and new knowledge will be acquired from an area not known or experienced. Regarding the MABS technique, it can solve the challenge of optimization and control, because it works in an empiric way during the decision-making process, based on experiments and observations.

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