Estudo comparativo em GAMA e Google Earth Engine: possibilidades para a área de sistemas multiagente
Resumo
Os recentes progressos nas tecnologias de informação e comunicação, que propiciam a melhora na coleta e na análise de dados hidrológicos, e no entendimento dos processos físicos da água permitem a implementação de modelos de simulação mais próximos da realidade. Este artigo tem como objetivo apresentar uma comparação das principais características e funcionalidades das ferramentas Google Earth e GAMA. Estas duas plataformas propiciam a integração das tecnologias presentes em sistemas de informação geográficas com sistemas multiagente, o que as tornam interessantes para o desenvolvimento de aplicações no âmbito da área ambiental e, mais especificamente neste trabalho, no gerenciamento de recursos hídricos, tendo como estudo de caso a bacia hidrográfica da Lagoa Mirim e Canal São Gonçalo. O resultado desta análise nos guiará para a definição da plataforma mais adequada para a modelagem futura do sistema.
Referências
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Berglund, E. Z. (2015). Using agent-based modeling for water resources planning and management. Journal of Water Resources Planning and Management, 141(11):04015025.
Born, M., Leitzke, B. S., Farias, G., Aguiar, M., and Adamatti, D. F. (2019). Modelagem baseada em agentes para análise de recursos hídricos. In Anais do XIII Workshop-Escola de Sistemas de Agentes, seus Ambientes e apliCacoes (WESAAC 2019), pages 107–118, Florianópolis/SC. [link].
Chu, T.-Q., Drogoul, A., Boucher, A., and Zucker, J.-D. (2009). Interactive learning of independent experts’ criteria for rescue simulations. J. UCS, 15:2701–2725.
Crooks, A., Castle, C., and Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6):417–430.
Ding, N., Erfani, R., Mokhtar, H., and Erfani, T. (2016). Agent based modelling for water resource allocation in the transboundary nile river. Water, 8(4):139.
Drogoul, A., Amouroux, E., Caillou, P., Gaudou, B., Grignard, A., Marilleau, N., Taillandier, P., Vavasseur, M., Vo, D.-A., and Zucker, J.-D. (2013). Gama: Multi-level and complex environment for agent-based models and simulations. In Proceedings of the 2013 Int. Conf. on Aut. Agen. and Multi-agent Sys., AAMAS ’13, pages 1361–1362, Richland, SC. Int. Fou. for Aut. Agen. and Mult. Sys.
Filatova, T., Verburg, P. H., Parker, D. C., and Stannard, C. A. (2013). Spatial agent-based models for socio-ecological systems: challenges and prospects. Environmental modelling & software, 45:1–7.
Gaudou, B., Sibertin-Blanc, C., Therond, O., Amblard, F., Auda, Y., Arcangeli, J.-P., Balestrat, M., Charron-Moirez, M.-H., Gondet, E., Hong, Y., Lardy, R., Louail, T., Mayor, E., Panzoli, D., Sauvage, S., Sánchez-Pérez, J.-M., Taillandier, P., Van Bai, N., Vavasseur, M., and Mazzega, P. (2014). The MAELIA multi-agent platform for integrated analysis of interactions between agricultural land-use and low-water management strategies. In Alam, S. J. and Parunak, H. V. D., editors, Multi-Agent-Based Simulation XIV, pages 85–100, Berlin, Heidelberg. Springer Berlin Heidelberg.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202:18–27.
Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., and Drogoul, A. (2013). Gama 1.6: Advancing the art of complex agent-based modeling and simulation. In Boella, G., Elkind, E., Savarimuthu, B. T. R., Dignum, F., and Purvis, M. K., editors, PRIMA 2013: Principles and Practice of Multi-Agent Systems, pages 117–131, Berlin, Heidelberg. Springer Berlin Heidelberg.
Hakdaoui, S., Emran, A., Pradhan, B., Qninba, A., Balla, T. E., Mfondoum, A. H. N., Lee, C.-W., and Alamri, A. M. (2020). Assessing the changes in the moisture/dryness of water cavity surfaces in imlili sebkha in southwestern morocco by using machine learning classification in google earth engine. Remote Sensing, 12(1):131.
Kandiah, V. K., Berglund, E. Z., and Binder, A. R. (2016). Cellular automata modeling framework for urban water reuse planning and management. Journal of Water Resources Planning and Management, 142(12):04016054.
Kumar, L. and Mutanga, O. (2019). Google Earth Engine Applications. MDPI.
Lin, Z., Lim, S. H., Lin, T., and Borders, M. (2020). Using agent-based modeling for water resources management in the bakken region. Journal of Water Resources Planning and Management, 146(1):05019020.
Macal, C. M. and North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005., pages 14–pp. IEEE.
Nguyen Vu, Q. A., Gaudou, B., Canal, R., and Hassas, S. (2009). Coherence and robustness in a disturbed mas. In 2009 IEEE-RIVF International Conference on Computing and Communication Technologies, pages 1–4.
Noël, P. H. and Cai, X. (2017). On the role of individuals in models of coupled human and natural systems: Lessons from a case study in the republican river basin. Environmental Modelling & Software, 92:1–16.
Ou, C., Yang, J., Du, Z., Liu, Y., Feng, Q., and Zhu, D. (2020). Long-term mapping of a greenhouse in a typical protected agricultural region using landsat imagery and the google earth engine. Remote Sensing, 12(1):55.
Shami, S. and Ghorbani, Z. (2019). Investigating water storage changes in iran using grace and chirps data in the google earth engine system. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Simmonds, J., Gómez, J. A., and Ledezma, A. (2019). The role of agent-based modeling and multi-agent systems in flood-based hydrological problems: a brief review. Journal of Water and Climate Change.
Taillandier, P., Vo, D.-A., Amouroux, E., and Drogoul, A. (2012). Gama: A simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In Desai, N., Liu, A., and Winikoff, M., editors, Principles and Practice of Multi-Agent Systems, pages 242–258, Berlin, Heidelberg. Springer Berlin Heidelberg.
Thérond, O., Sibertin-Blanc, C., Lardy, R., Gaudou, B., Balestrat, M., Hong, Y., Louail, T., Nguyen, V. B., Panzoli, D., Sanchez-Perez, J.-M., Sauvage, S., Taillandier, P., Vavasseur, M., and Mazzega, P. (2014). Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems. In 7th International Environmental Modelling and Software Society (iEMSs 2014), page pp. 1, San Diego, California, United States.
Tourigny, A. and Filion, Y. (2019). Sensitivity analysis of an agent-based model used to simulate the spread of low-flow fixtures for residential water conservation and evaluate energy savings in a canadian water distribution system. Journal of Water Resources Planning and Management, 145(1):04018086.
Vicuña, S., McPhee, J., and Garreaud, R. D. (2012). Agriculture vulnerability to climate change in a snowmelt-driven basin in semiarid chile. Journal of Water Resources Planning and Management, 138(5):431–441.
Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., Ma, L., Jin, N., and Meng, Q. (2019). Changes in water surface area during 1989–2017 in the huai river basin using landsat data and google earth engine. Remote Sensing, 11(15):1824.
Amouroux, E., Desvaux, S., and Drogoul, A. (2008). Towards virtual epidemiology: An agent-based approach to the modeling of h5n1 propagation and persistence in north-vietnam. In Bui, T. D., Ho, T. V., and Ha, Q. T., editors, Intelligent Agents and Multi-Agent Systems, pages 26–33, Berlin, Heidelberg. Springer Berlin Heidelberg.
An, L. (2012). Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229:25–36.
Berglund, E. Z. (2015). Using agent-based modeling for water resources planning and management. Journal of Water Resources Planning and Management, 141(11):04015025.
Born, M., Leitzke, B. S., Farias, G., Aguiar, M., and Adamatti, D. F. (2019). Modelagem baseada em agentes para análise de recursos hídricos. In Anais do XIII Workshop-Escola de Sistemas de Agentes, seus Ambientes e apliCacoes (WESAAC 2019), pages 107–118, Florianópolis/SC. [link].
Chu, T.-Q., Drogoul, A., Boucher, A., and Zucker, J.-D. (2009). Interactive learning of independent experts’ criteria for rescue simulations. J. UCS, 15:2701–2725.
Crooks, A., Castle, C., and Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6):417–430.
Ding, N., Erfani, R., Mokhtar, H., and Erfani, T. (2016). Agent based modelling for water resource allocation in the transboundary nile river. Water, 8(4):139.
Drogoul, A., Amouroux, E., Caillou, P., Gaudou, B., Grignard, A., Marilleau, N., Taillandier, P., Vavasseur, M., Vo, D.-A., and Zucker, J.-D. (2013). Gama: Multi-level and complex environment for agent-based models and simulations. In Proceedings of the 2013 Int. Conf. on Aut. Agen. and Multi-agent Sys., AAMAS ’13, pages 1361–1362, Richland, SC. Int. Fou. for Aut. Agen. and Mult. Sys.
Filatova, T., Verburg, P. H., Parker, D. C., and Stannard, C. A. (2013). Spatial agent-based models for socio-ecological systems: challenges and prospects. Environmental modelling & software, 45:1–7.
Gaudou, B., Sibertin-Blanc, C., Therond, O., Amblard, F., Auda, Y., Arcangeli, J.-P., Balestrat, M., Charron-Moirez, M.-H., Gondet, E., Hong, Y., Lardy, R., Louail, T., Mayor, E., Panzoli, D., Sauvage, S., Sánchez-Pérez, J.-M., Taillandier, P., Van Bai, N., Vavasseur, M., and Mazzega, P. (2014). The MAELIA multi-agent platform for integrated analysis of interactions between agricultural land-use and low-water management strategies. In Alam, S. J. and Parunak, H. V. D., editors, Multi-Agent-Based Simulation XIV, pages 85–100, Berlin, Heidelberg. Springer Berlin Heidelberg.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202:18–27.
Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., and Drogoul, A. (2013). Gama 1.6: Advancing the art of complex agent-based modeling and simulation. In Boella, G., Elkind, E., Savarimuthu, B. T. R., Dignum, F., and Purvis, M. K., editors, PRIMA 2013: Principles and Practice of Multi-Agent Systems, pages 117–131, Berlin, Heidelberg. Springer Berlin Heidelberg.
Hakdaoui, S., Emran, A., Pradhan, B., Qninba, A., Balla, T. E., Mfondoum, A. H. N., Lee, C.-W., and Alamri, A. M. (2020). Assessing the changes in the moisture/dryness of water cavity surfaces in imlili sebkha in southwestern morocco by using machine learning classification in google earth engine. Remote Sensing, 12(1):131.
Kandiah, V. K., Berglund, E. Z., and Binder, A. R. (2016). Cellular automata modeling framework for urban water reuse planning and management. Journal of Water Resources Planning and Management, 142(12):04016054.
Kumar, L. and Mutanga, O. (2019). Google Earth Engine Applications. MDPI.
Lin, Z., Lim, S. H., Lin, T., and Borders, M. (2020). Using agent-based modeling for water resources management in the bakken region. Journal of Water Resources Planning and Management, 146(1):05019020.
Macal, C. M. and North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005., pages 14–pp. IEEE.
Nguyen Vu, Q. A., Gaudou, B., Canal, R., and Hassas, S. (2009). Coherence and robustness in a disturbed mas. In 2009 IEEE-RIVF International Conference on Computing and Communication Technologies, pages 1–4.
Noël, P. H. and Cai, X. (2017). On the role of individuals in models of coupled human and natural systems: Lessons from a case study in the republican river basin. Environmental Modelling & Software, 92:1–16.
Ou, C., Yang, J., Du, Z., Liu, Y., Feng, Q., and Zhu, D. (2020). Long-term mapping of a greenhouse in a typical protected agricultural region using landsat imagery and the google earth engine. Remote Sensing, 12(1):55.
Shami, S. and Ghorbani, Z. (2019). Investigating water storage changes in iran using grace and chirps data in the google earth engine system. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Simmonds, J., Gómez, J. A., and Ledezma, A. (2019). The role of agent-based modeling and multi-agent systems in flood-based hydrological problems: a brief review. Journal of Water and Climate Change.
Taillandier, P., Vo, D.-A., Amouroux, E., and Drogoul, A. (2012). Gama: A simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In Desai, N., Liu, A., and Winikoff, M., editors, Principles and Practice of Multi-Agent Systems, pages 242–258, Berlin, Heidelberg. Springer Berlin Heidelberg.
Thérond, O., Sibertin-Blanc, C., Lardy, R., Gaudou, B., Balestrat, M., Hong, Y., Louail, T., Nguyen, V. B., Panzoli, D., Sanchez-Perez, J.-M., Sauvage, S., Taillandier, P., Vavasseur, M., and Mazzega, P. (2014). Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems. In 7th International Environmental Modelling and Software Society (iEMSs 2014), page pp. 1, San Diego, California, United States.
Tourigny, A. and Filion, Y. (2019). Sensitivity analysis of an agent-based model used to simulate the spread of low-flow fixtures for residential water conservation and evaluate energy savings in a canadian water distribution system. Journal of Water Resources Planning and Management, 145(1):04018086.
Vicuña, S., McPhee, J., and Garreaud, R. D. (2012). Agriculture vulnerability to climate change in a snowmelt-driven basin in semiarid chile. Journal of Water Resources Planning and Management, 138(5):431–441.
Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., Ma, L., Jin, N., and Meng, Q. (2019). Changes in water surface area during 1989–2017 in the huai river basin using landsat data and google earth engine. Remote Sensing, 11(15):1824.
Publicado
07/07/2020
Como Citar
BORN, Míriam; MOTA, Fernanda P.; FARIAS, Giovani; GONÇALVES, Matheus; LEITZKE, Bruna; AGUIAR, Marilton; ADAMATTI, Diana F..
Estudo comparativo em GAMA e Google Earth Engine: possibilidades para a área de sistemas multiagente. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 14. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 61-73.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2020.33380.
