Artificial Intelligence Applied to Natural Resources Management

Author:

Adamatti Diana F.1,de Aguiar Marilton S.2

Affiliation:

1. Universidade Federal do Rio Grande (FURG), Brasil

2. Universidade Federal de Pelotas (UFPel), Brasil

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.

Publisher

IGI Global

Reference45 articles.

1. Adamatti, D. F., Sichman, J., Bommel, P., Ducrot, R., Rabak, C., & Camargo, M. (2005). JogoMan: A prototype using multi-agent-based simulation and role-playing games in water management. In N. Ferrand (Ed.), Joint Conference on Multi-Agent Modeling for Environmental Management, (CABM-HEMA-SMAGET), Bourg-Saint-Maurice, Les Arcs, France.

2. Adamatti, D. F., Sichman, J. S., & Coelho, H. (2007). Virtual players: From manual to semiautonomous RPG. In C. Frydman (Ed.), AI, Simulation and Planning in High Autonomy Systems (AIS) and Conceptual Modeling and Simulation (CMS). Joint to International Modeling and Simulation Multiconference 2007 (IMSM07), Buenos Aires – Argentina.

3. ICTM: An Interval Tessellation-Based Model for Reliable Topographic Segmentation

4. Aguiar, M. S., Dimuro, G. P., Costa, A. C. R., Silva, R. K. S., Costa, F. A., & Kreinovich, V. (2004a). The multi-layered interval categorizer tesselation-based model. In C. Iochpe & G. Câmara (Eds.), VI Brazilian Symposium on Geoinformatics, 22- 24 November, Campos do Jordão, São Paulo, Brazil (pp. 437–454).

5. An artificial neural network model for generating hydrograph from hydro-meteorological parameters

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial Intelligence;Handbook of Research on Manufacturing Process Modeling and Optimization Strategies;2017

2. Related References;Technologies for Urban and Spatial Planning: Virtual Cities and Territories

3. Compilation of References;Technologies for Urban and Spatial Planning: Virtual Cities and Territories

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3