Propuesta de Hoja de Ruta para simulaciones climáticas en Latinoamérica basadas en Inteligencia Artificial
Resumen
Este documento presenta una propuesta de hoja de ruta para integrar a la comunidad científica de Latinoamérica, con el fin de compartir recursos computacionales e implementar aplicaciones climáticas para pronosticar eventos relacionados con el calentamiento global en la región. Las aplicaciones climáticas se desarrollan comúnmente en arquitecturas distribuidas y paralelas debido a la gran cantidad de datos que deben procesarse. Como procedimiento de optimización, también proponemos implementar métodos de Inteligencia Artificial tanto para la evaluación del rendimiento de las aplicaciones como para la mejora de los resultados de las simulaciones climáticas. Una ventaja de utilizar este tipo de algoritmos radica en la posibilidad de identificar relaciones entre diferentes eventos meteorológicos, con el desarrollo de modelos que integran diversos tipos de fenómenos, incluyendo eventos extremos. Los modelos de pronóstico climático utilizan diversas fuentes de datos, principalmente recopilando imágenes y series de tiempo. Un modelo de predicción climática puede implementarse con una red de sensores remotos, lo que implica una distribución geográfica que podría extrapolarse a las tareas de procesamiento. La comunidad científica en Latinoamérica puede compartir recursos computacionales y cuenta con el talento para desarrollar la implementación de estos algoritmos. Además, invitamos a los miembros de la comunidad científica a unirse a nosotros para afrontar este desafío.
Referencias bibliográficas
Anh Khoa, T., Quang Minh, N., Hai Son, H., Nguyen Dang Khoa, C., Ngoc Tan, D., VanDung, N., . . . Trung Tin, N. (2021). Wireless sensor networks and machine learning meet climate change prediction. International Journal of Communication Systems, 34(3), e4687. https://doi.org/10.1002/dac.4687
Bao, Z., Zhang, J., Wang, G., Guan, T., Jin, J., Liu, Y., . . . Ma, T. (2021). The sensitivity of vegetation cover to climate change in multiple climatic zones using machine learning algorithms. Ecological Indicators, 124, 1-18; 107443. https://doi.org/10.1016/j.ecolind.2021.107443
Bellprat, O., & Doblas-Reyes, F. (2016). Attribution of extreme weather and climate events overestimated by unreliable climate simulations. Geophysical Research Letters, 43(5), 2158-2164. https://doi.org/10.1002/2015GL067189
Berrang-Ford, L., Sietsma, A., Callaghan, M., Minx, J., Scheelbeek, P., Haddaway, N., . . . Dangour, A. (2021). Mapping global research on climate and health using machine learning (a systematic evidence map). Wellcome Open Research, 6:7, 1-17. https://doi.org/10.12688/wellcomeopenres.16415.1
Boito, F., Kassick, R., Navaux, R., & Denneulin, Y. (2016). Automatic I/O scheduling algorithm selection for parallel file systems. Concurrency and Computation: Practice and Experience, 28(8), 2457-2472. https://doi.org/10.1002/cpe.3606
Buchty, R., Heuveline, V., Karl, W., & Weiss, J. (2012). A survey on hardware-aware and heterogeneous computing on multicore processors and accelerators. Concurrency and Computation: Practice and Experience, 24(1532-0626), 663-675. https://doi.org/10.1002/cpe.1904
Callaghan, M., Schleussner, C., Nath, S., Lejeune, Q., Knutson, T., Reichstein, M., . . . Minx, J. (2021). Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature Climate Change, 11, 966-972. https://doi.org/10.1038/s41558-021-01168-6
Castro, M., W. Góes, L., & Méhaut, J. (2014). Adaptive thread mapping strategies for transactional memory applications. Journal of Parallel and Distributed Computing, 74, 2845-2859. https://doi.org/10.1016/j.jpdc.2014.05.008
Chen, P. (2019). Visualization of real-time monitoring datagraphic of urban environmental quality. EURASIP Journal on Image and Video Processing(42), 1-19. https://doi.org/10.1186/s13640-019-0443-6
Clarke, L., Glendinning, I., & Hempel, R. (1994). The MPI Message Passing Interface Standard. In K. Decker, & R. Rehmann, Programming Environments for Massively Parallel Distributed Systems (pp. 213–218). Monte Verità: Birkhäuser. https://doi.org/10.1007/978-3-0348-8534-8_21
Climate Change AI - CCAI. (2019). What's New. Retrieved 8 7, 2025, from Climate Change AI (CCAI): https://www.climatechange.ai/
CNPq - INRIA. (2015). HOSCAR project home page - High performance cOmputing and SCientific dAta management dRiven by highly demanding applications. Retrieved 8 7, 2025, from HOSCAR project: http://www-sop.inria.fr/hoscar/
Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate. Environmental Research Letters, 13(11), 1-13; 114003. https://doi.org/10.1088/1748-9326/aae159
de la Cruz, R., & Araya-Polo, M. (2015). Modeling Stencil Computations on Modern HPC Architectures. In S. Jarvis, S. Wright, & S. Hammond (Ed.), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation (pp. 149--171). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17248-4_8
Donti, P. (2020). How machine learning can help tackle climate change. Association for Computing Machinery, 27(2), 58–61. https://doi.org/10.1145/3433142
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
GPPD/UFRGS. (2013). Latin American Grid for Climatology Project. Retrieved 8 7, 2025, from LAGClima: https://www.inf.ufrgs.br/gppd/projetos/lagclima/index.html
Heaven, W. (2021, 9 29). DeepMind’s AI predicts almost exactly when and where it’s going to rain. Retrieved 7 28, 2025, from MIT Technology Review: https://www.technologyreview.com/2021/09/29/1036331/%20deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/
Horizon. (2020). H2020 High Performance Computing for Energy (HPC4E) 2015-2017. Retrieved 8 7, 2025, from Scientific Data Management: https://team.inria.fr/zenith/h2020-high-performance-computing-for-energy-2015-2017/
Ibrahim, S., Ziedan, I., & Ahmed, A. (2021). Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 11080-11094. https://doi.org/10.1109/JSTARS.2021.3120987
Kairouz, P., McMahan, H., Avent, B., Bellet, B., Bennis, A., Bhagoji, M., . . . Sen. (2021). Advances and Open Problems in Federated Learning (Vol. 14). Now Foundations and Trends. Retrieved 8 2, 2025, from https://ieeexplore.ieee.org/document/9464278
Kress, J., Afzal, S., Dasari, H., Hari, P., Ghani, S., Zamreeq, A., . . . Hoteit, I. (2023). Visualization Environment for Analyzing Extreme Rainfall Events: A Case Study. In S. Dutta, K. Feige, K. Rink, & D. Zeckzer (Ed.), Workshop on Visualisation in Environmental Sciences (EnvirVis) (pp. 025-032 - 1-8). The Eurographics Association. https://doi.org/10.2312/envirvis.20231103
Kumar, M., Sathish, B., & Balamurugan, B. (2017). A Review on Performance Evaluation Techniques in Cloud. 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) (pp. 19-24). Tindivanam: IEEE. https://doi.org/10.1109/ICRTCCM.2017.29
Lakshmanan, V., Gilleland, E., McGovern, A., & Tingley, M. (Eds.). (2015). Machine Learning and Data Mining Approaches to Climate Science (1 ed.). Switzerland : Springer Cham. https://doi.org/10.1007/978-3-319-17220-0
Lang, N., Jetz, W., Schindler, K., & Wegner, J. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7, 1778–1789. https://doi.org/10.1038/s41559-023-02206-6
Li, Z., Huang, Q., Jiang, Y., & Hu, F. (2020). SOVAS: a scalable online visual analytic system for big climate data analysis. International Journal of Geographical Information Science, 34(6), 1188--1209. https://doi.org/10.1080/13658816.2019.1605073
Lipschultz, F., Herring, D., Ray, A., Alder, J., Dahlman, L., DeGaetano, A., . . . Sweet, W. (2020). Climate Explorer: Improved Access to Local Climate Projections. Bulletin of the American Meteorological Society, 101(3), E265 - E273. https://doi.org/10.1175/BAMS-D-18-0298.1
Mansfield, L., Nowack, P., Kasoar, M., Everitt, R., Collins, W., & Voulgarakis, A. (2020, 11 19). Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Climate and Atmospheric Science, 3(44), 1-9. https://doi.org/10.1038/s41612-020-00148-5
Martínez, V., Dupros, F., Castro, M., & Navaux, P. (2017). Performance Improvement of Stencil Computations for Multi-core Architectures based on Machine Learning. Procedia Computer Science, 108, 305-314. https://doi.org/10.1016/j.procs.2017.05.164
Massonnet, F., Bellprat, O., Guemas, V., & Doblas-Reyes, F. (2016). Using climate models to estimate the quality of global observational data sets. Science, 354(6311), 452-455. https://doi.org/10.1126/science.aaf6369
McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera, B. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In A. a. Singh (Ed.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 54, pp. 1273--1282. PMLR. Retrieved 8 2, 2025, from https://proceedings.mlr.press/v54/mcmahan17a.html
Milojevic-Dupont, N., & Creutzig, F. (2021). Machine learning for geographically differentiated climate change mitigation in urban areas. Sustainable Cities and Society, 64(102526), 1-15. https://doi.org/10.1016/j.scs.2020.102526
Mittal, S. (2016). A Survey of Techniques for Architecting and Managing Asymmetric Multicore Processors. ACM Computing Surveys, 48(45), 1-38. https://doi.org/10.1145/2856125
Partee, S., Ellis, M., Rigazzi, A., Shao, A., Bachman, S., Marques, G., & Robbins, B. (2022). Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling. Journal of Computational Science, 62(101707), 1-12. https://doi.org/10.1016/j.jocs.2022.101707
Patterson, D., & Hennessy, J. (2013). COMPUTER ORGANIZATION AND DESIGN: THE HARDWARE/SOFTWARE IN TERFACE (5 ed.). (T. Green , Ed.) Oxford: ELSEVIER. Retrieved 8 2, 2025, from https://www.cse.iitd.ac.in/~rijurekha/col216/edition5.pdf
Porto, F., Ferro, M., Ogasawara, E., Moeda, T., Tenorio de Barros, C., Chaves , A., . . . Bezerra, E. (2022). Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results. Supercomputing Frontiers and Innovations, 9(1), 49–73. https://doi.org/10.14529/jsfi220104
Programa CYTED. (2017). Programa CYTED. Retrieved 8 7, 2025, from Cyted - Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo: https://www.cyted.org/?q=es/detalle_proyecto&un=927
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., . . . Mohamed, S. (2021, 09 01). Skilful precipitation nowcasting using deep generative models of radar. Nature, 597, 672–677. https://doi.org/10.1038/s41586-021-03854-z
RedCLARA. (2004). Ciencia, Eduación, Cultura e Innovación. Retrieved 8 7, 2025, from RedCLARA: https://www.redclara.net/es/
RedCLARA. (2011). Colombia lanzará Grid Nacional. Retrieved 8 7, 2025, from RedCLARA: https://www.redclara.net/es/component/content/article/1364-colombia-lanzara-grid-nacional?catid=99&highlight=WyJjb2xvbWJpYSIsImNvbG9tYmlhbiIsImdyaWQiXQ==&Itemid=437
Rolnick, D., Donti, P., Kaack, L., Kochanski, K., Lacoste, A., Sankaran, K., . . . Bengio, Y. (2022). Tackling Climate Change with Machine Learning. ACM Computing Surveys (CSUR), 55(2), 1 - 96. https://doi.org/10.1145/3485128
Samuel, A. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
Scharl, A., Hubmann-Haidvogel, A., Weichselbraun, A., Lang, H., & Sabou, M. (2013). Media Watch on Climate Change -- Visual Analytics for Aggregating and Managing Environmental Knowledge from Online Sources. 2013 46th Hawaii International Conference on System Sciences (pp. 955-964). Wailea, Hawái: IEEE. https://doi.org/10.1109/HICSS.2013.398
Shokri, R., & Shmatikov, V. (2015). Privacy-Preserving Deep Learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, (pp. 1310–1321). New York. https://doi.org/10.1145/2810103.2813687
Shukla, S., Murthy, C., & Chande, P. (2015). A Survey of Approaches used in Parallel Architectures and Multi-core Processors, For Performance Improvement. In H. Selvaraj, D. Zydek, & G. Chmaj, Advances in Intelligent Systems and Computing (Vol. 366, pp. 537–545). Las Vegas, NV: Springer. https://doi.org/10.1007/978-3-319-08422-0_77
SINAPAD. (2014). Projeto Sistema de Computação Petaflópica do SINAPAD/2014. Processo número 01.14.192.00. Retrieved 8 2, 2025, from SDumont: https://sdumont.lncc.br/
Snavely, A., Gao, X., Lee, C., Carrington, L., Wolter, N., Labarta, J., . . . Jones, P. (2004). Performance modeling of HPC applications. In G. Joubert, W. Nagel, F. Peters, & W. Walter, Advances in Parallel Computing (Vol. 13, pp. 777-784). North-Holland: ELSEVIER. https://doi.org/10.1016/S0927-5452(04)80095-9
Tanenbaum, A., & Van Steen, M. (2006). Distributed Systems: Principles and Paradigms (2 ed.). (T. Dunkelberger, Ed.) Upper Saddle River, New Jersey: Pearson Prentice Hall. Retrieved 8 2, 2025, from https://vowi.fsinf.at/images/b/bc/TU_Wien-Verteilte_Systeme_VO_%28G%C3%B6schka%29_-_Tannenbaum-distributed_systems_principles_and_paradigms_2nd_edition.pdf
Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988-999. https://doi.org/10.1109/72.788640
Vladušic, D., Cernivec, A., & Slivnik, B. (2009). Improving Job Scheduling in GRID Environments with Use of Simple Machine Learning Methods. 2009 Sixth International Conference on Information Technology: New Generations (pp. 177-182). Las Vegas: IEEE. https://doi.org/10.1109/ITNG.2009.228
Wang, J., Liu, X., Shen, H.-W., & Lin, G. (2017). Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. IEEE Transactions on Visualization and Computer Graphics, 23(1), 81-90. https://doi.org/10.1109/TVCG.2016.2598830
Weng, L., Liu, C., & Gaudiot, J. (2013). Scheduling optimization in multicore multithreaded microprocessors through dynamic modeling. Association for Computing Machinery, 1-10. https://doi.org/10.1145/2482767.2482774
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678. https://doi.org/10.1109/TNN.2005.845141
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021). Federated learning on non-IID data: A survey. Neurocomputing, 465, 371-390. https://doi.org/10.1016/j.neucom.2021.07.098
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