Integração entre Sistemas Multiagente e a Plataforma Google Earth Engine para análise do fluxo d’água na Bacia Hidrográfica da Lagoa Mirim e Canal São Gonçalo
Resumo
Este trabalho tem como objetivo principal utilizar Simulação Baseada em Multiagente (MABS) em conjunto com a plataforma Google Earth Engine para analisar o fluxo dos rios entre as regiões. A simulação será feita a partir dos dados do estado do Rio Grande do Sul, e focando a aplicação-piloto do trabalho no Comitê de Gerenciamento das Bacias Hidrográficas. A Bacia envolve a Lagoa Mirim e do Canal São Gonçalo, especificamente nas cidades de Rio Grande e Pelotas. Do que rege nosso conhecimento, ainda não foi aplicada essa metodologia no contexto do estado, buscando uma forma mais interativa e participativa para a tomada de decisão sobre questões hídricas.Referências
Alvares, L. O. and Sichman, J. S. (1997). Introduçao aos sistemas multiagentes. In XVII Congresso da SBC-Anais JAI’97.
Axelrod, R. (1996). The complexity of cooperation: agent-based models of competition and collaboration. Princeton Univ. Press.
Azevedo, L. L. and Meneze, C. S. (2007). Netplay – uma ferramenta para construção de modelos de simulação baseado em multiagente. XVIII Simpósio Brasileiro de Informática na Educação, SBIE, Mackenzie.
Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., and Santi, A. (2013). Multi-agent oriented programming with jacamo. Science of Computer Programming, 78(6):747–761.
Bordini, R. H., Hübner, J. F., and Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons.
Bordini, R. H., Vieira, R., and Moreira, A. F. (2001). Fundamentos de sistemas multiagentes. In Anais do XXI Congresso da Sociedade Brasileira de Computação (SBC2001), volume 2, pages 3–41.
Cann, K., Thomas, D. R., Salmon, R., Wyn-Jones, A., and Kay, D. (2013). Extreme water-related weather events and waterborne disease. Epidemiology & Infection, 141(4):671–686.
Chen, X. (2017). js-simulator. [link].
Deng, Y., Jiang, W., Tang, Z., Ling, Z., and Wu, Z. (2019). Long-term changes of open-surface water bodies in the yangtze river basin based on the google earth engine cloud platform. Remote Sensing, 11(19):2213.
Dimuro, G. P., Costa, A. C. R., and Palazzo, L. A. M. (2005). Systems of exchange values as tools for multi-agent organizations. Journal of the Brazilian Computer Society.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore III, B. (2016). Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote sensing of environment, 185:142–154.
Feyisa, G. L., Meilby, H., Fensholt, R., and Proud, S. R. (2014). Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sensing of Environment, 140:23–35.
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.
Fisher, A., Flood, N., and Danaher, T. (2016). Comparing landsat water index methods for automated water classification in eastern australia. Remote Sensing of Environment, 175:167–182.
Fuller, M. M., Wang, D., Gross, L. J., and Berry, M. W. (2007). Computational science for natural resource management. Computing in Science & Engineering, 9(4):40.
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.
Hird, J. N., DeLancey, E. R., McDermid, G. J., and Kariyeva, J. (2017). Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote sensing, 9(12):1315.
Hübner, J. F., Sichman, J. S., and Boissier, O. (2007). Developing organised multiagent systems using the MOISE+ model: programming issues at the system and agent levels. Int. J. Agent-Oriented Software Engineering, 1(3/4):370–395.
Kumar, L. and Mutanga, O. (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10):1509.
Kumar, L. and Mutanga, O. (2019). Google Earth Engine Applications. MDPI.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using landsat images based on the google earth engine platform. Remote sensing of environment, 209:227–239.
Luke, S., Balan, G. C., Panait, L., Cioffi-Revilla, C., and Paus, S. (2003). Mason: A java multi-agent simulation library. In Proceedings of Agent 2003 Conference on Challenges in Social Simulation, volume 9.
McCullough, I. M., Loftin, C. S., and Sader, S. A. (2013). Lakes without landsat? an alternative approach to remote lake monitoring with modis 250 m imagery. Lake and reservoir management, 29(2):89–98.
Mutanga, O. and Kumar, L. (2019). Google earth engine applications.
Nwana, H. S. (1996). Software agents: An overview. The knowledge engineering review, 11(3):205–244.
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.
Page, C. L., Bousquet, F., Bakam, I., Bah, A., and Baron, C. (2000). CORMAS : A multiagent simulation toolkit to model natural and social dynamics at multiple scales. In Wageningen : Resource Modeling Association.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633):418–422.
Rao, A. S., Georgeff, M. P., et al. (1995). Bdi agents: from theory to practice. In Icmas, volume 95, pages 312–319.
Ricci, A., Viroli, M., and Omicini, A. (2006). Cartago: A framework for prototyping artifact-based environments in mas. In International Workshop on Environments for Multi-Agent Systems, pages 67–86. Springer.
Russell, S. and Norvig, P. (2013). Inteligência Artificial. Elsevier Ltda, Rio de Janeiro/RJ, 3a edition.
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.
Taillandier, P. and Buard, E. (2009). Designing agent behaviour in agent-based simulation through participatory method. In International Conference on Principles and Practice of Multi-Agent Systems, pages 571–578. Springer.
Tisue, S. and Wilensky, U. (1999). Center for connected learning and computer-based modeling northwestern university, evanston, illinois. NetLogo: A Simple Environment for Modeling Complexity, Citeseer.
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L., Bierkens, M. F., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., et al. (2011). Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring earth’s terrestrial water. Water Resources Research, 47(5).
Wooldridge, M. (2002). An introduction to multi agent systems, department of computer science, university of liverpool, uk.
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.
Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R. B., Menarguez, M. A., Zhang, G., and Wang, J. (2018). Divergent trends of open-surface water body area in the contiguous united states from 1984 to 2016. Proceedings of the National Academy of Sciences, 115(15):3810–3815.
Axelrod, R. (1996). The complexity of cooperation: agent-based models of competition and collaboration. Princeton Univ. Press.
Azevedo, L. L. and Meneze, C. S. (2007). Netplay – uma ferramenta para construção de modelos de simulação baseado em multiagente. XVIII Simpósio Brasileiro de Informática na Educação, SBIE, Mackenzie.
Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., and Santi, A. (2013). Multi-agent oriented programming with jacamo. Science of Computer Programming, 78(6):747–761.
Bordini, R. H., Hübner, J. F., and Wooldridge, M. (2007). Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons.
Bordini, R. H., Vieira, R., and Moreira, A. F. (2001). Fundamentos de sistemas multiagentes. In Anais do XXI Congresso da Sociedade Brasileira de Computação (SBC2001), volume 2, pages 3–41.
Cann, K., Thomas, D. R., Salmon, R., Wyn-Jones, A., and Kay, D. (2013). Extreme water-related weather events and waterborne disease. Epidemiology & Infection, 141(4):671–686.
Chen, X. (2017). js-simulator. [link].
Deng, Y., Jiang, W., Tang, Z., Ling, Z., and Wu, Z. (2019). Long-term changes of open-surface water bodies in the yangtze river basin based on the google earth engine cloud platform. Remote Sensing, 11(19):2213.
Dimuro, G. P., Costa, A. C. R., and Palazzo, L. A. M. (2005). Systems of exchange values as tools for multi-agent organizations. Journal of the Brazilian Computer Society.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore III, B. (2016). Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote sensing of environment, 185:142–154.
Feyisa, G. L., Meilby, H., Fensholt, R., and Proud, S. R. (2014). Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sensing of Environment, 140:23–35.
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.
Fisher, A., Flood, N., and Danaher, T. (2016). Comparing landsat water index methods for automated water classification in eastern australia. Remote Sensing of Environment, 175:167–182.
Fuller, M. M., Wang, D., Gross, L. J., and Berry, M. W. (2007). Computational science for natural resource management. Computing in Science & Engineering, 9(4):40.
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.
Hird, J. N., DeLancey, E. R., McDermid, G. J., and Kariyeva, J. (2017). Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote sensing, 9(12):1315.
Hübner, J. F., Sichman, J. S., and Boissier, O. (2007). Developing organised multiagent systems using the MOISE+ model: programming issues at the system and agent levels. Int. J. Agent-Oriented Software Engineering, 1(3/4):370–395.
Kumar, L. and Mutanga, O. (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10):1509.
Kumar, L. and Mutanga, O. (2019). Google Earth Engine Applications. MDPI.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using landsat images based on the google earth engine platform. Remote sensing of environment, 209:227–239.
Luke, S., Balan, G. C., Panait, L., Cioffi-Revilla, C., and Paus, S. (2003). Mason: A java multi-agent simulation library. In Proceedings of Agent 2003 Conference on Challenges in Social Simulation, volume 9.
McCullough, I. M., Loftin, C. S., and Sader, S. A. (2013). Lakes without landsat? an alternative approach to remote lake monitoring with modis 250 m imagery. Lake and reservoir management, 29(2):89–98.
Mutanga, O. and Kumar, L. (2019). Google earth engine applications.
Nwana, H. S. (1996). Software agents: An overview. The knowledge engineering review, 11(3):205–244.
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.
Page, C. L., Bousquet, F., Bakam, I., Bah, A., and Baron, C. (2000). CORMAS : A multiagent simulation toolkit to model natural and social dynamics at multiple scales. In Wageningen : Resource Modeling Association.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633):418–422.
Rao, A. S., Georgeff, M. P., et al. (1995). Bdi agents: from theory to practice. In Icmas, volume 95, pages 312–319.
Ricci, A., Viroli, M., and Omicini, A. (2006). Cartago: A framework for prototyping artifact-based environments in mas. In International Workshop on Environments for Multi-Agent Systems, pages 67–86. Springer.
Russell, S. and Norvig, P. (2013). Inteligência Artificial. Elsevier Ltda, Rio de Janeiro/RJ, 3a edition.
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.
Taillandier, P. and Buard, E. (2009). Designing agent behaviour in agent-based simulation through participatory method. In International Conference on Principles and Practice of Multi-Agent Systems, pages 571–578. Springer.
Tisue, S. and Wilensky, U. (1999). Center for connected learning and computer-based modeling northwestern university, evanston, illinois. NetLogo: A Simple Environment for Modeling Complexity, Citeseer.
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L., Bierkens, M. F., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., et al. (2011). Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring earth’s terrestrial water. Water Resources Research, 47(5).
Wooldridge, M. (2002). An introduction to multi agent systems, department of computer science, university of liverpool, uk.
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.
Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R. B., Menarguez, M. A., Zhang, G., and Wang, J. (2018). Divergent trends of open-surface water body area in the contiguous united states from 1984 to 2016. Proceedings of the National Academy of Sciences, 115(15):3810–3815.
Publicado
31/08/2022
Como Citar
MOTA, Fernanda P.; AGUIAR, Marilton S.; ADAMATTI, Diana F..
Integração entre Sistemas Multiagente e a Plataforma Google Earth Engine para análise do fluxo d’água na Bacia Hidrográfica da Lagoa Mirim e Canal São Gonçalo. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 16. , 2022, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 8-19.
ISSN 2326-5434.
