2026-04-17T12:25:35Zhttps://repositorio.cuc.edu.co/server/oai/request

oai:repositorio.cuc.edu.co:11323/100172024-09-17T17:45:13Zcom_11323_3col_11323_2032

Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) Derechos de autor 2022 INGE CUC https://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Herrera Posada, Daissy Milenys Aristizábal, Edier 2023-04-25T17:02:28Z 2023-04-25T17:02:28Z 2022 D. Hererra posada & E. Aristizábal, “Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia”, INGECUC, vol. 18, no. 2, pp. 249–265. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.20 0122-6517 https://hdl.handle.net/11323/10017 10.17981/ingecuc.18.2.2022.20 2382-4700 Corporación Universidad de la Costa REDICUC - Repositorio CUC https://repositorio.cuc.edu.co/ Introduction— Drought is one of the most critical hydrometeorological phenomenon in terms of its impacts on society. Although Colombia is a tropical country, there are areas of the territory which have periods of drought, and this causes significant economic damage. Objective— Due to recent advances in terms of the spatial and temporal resolutions of remote sensing, and artificial intelligence techniques, it is possible to develop automatic learning models supported by historical information. Methodology— In this study, a Random Forest (RF) and Bagged Decision Tree Classifier (DTC) model was built to perform spatial and temporal drought prediction in the department of Magdalena using the following features: Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), precipitation, Normalized Difference Water Index (NDWI), Normalized Multiband Drought Index (NMDI), evapotranspiration (ET), surface soil moisture (SSM), subsurface soil moisture (SUSM), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI). Results— For labelling, which allows one to train and evaluate the model, the Standardized Precipitation Index (SPI) was used to identify drought events. Conclusions— The implementation of the developed model can allow governmental entities to take actions to mitigate impacts generated by recurring droughts in their territories. Introducción— La sequía es uno de los fenómenos hidrometeorológicos más críticos por sus impactos en la sociedad. A pesar de que Colombia es un país tropical, existen zonas del territorio que presentan periodos de sequía, lo que ocasiona importantes perjuicios económicos. Objetivo— Debido a los recientes avances en cuanto a las resoluciones espaciales y temporales de la teledetección, y a las técnicas de inteligencia artificial, es posible desarrollar modelos de aprendizaje automático apoyados en información histórica. Metodología— En este estudio se construyó un modelo clasificador de Bosque Aleatorio (RF) y Árbol de Decisión en Bolsa (DTC) para realizar la predicción espacial y temporal de sequía en el departamento del Magdalena utilizando las siguientes características: Índice de Vegetación de Diferencia Normalizada (NDVI), temperatura de la superficie terrestre (LST), precipitación, Índice de Agua de Diferencia Normalizada (NDWI), Índice de Sequía Multibanda Normalizada (NMDI), evapotranspiración (ET), humedad superficial del suelo (SSM), humedad subsuperficial del suelo (SUSM), Índice ENSO Multivariado (MEI), Índice de Oscilación del Sur (SOI) e Índice del Niño Oceánico (ONI). Resultados— Para el etiquetado, que permite entrenar y evaluar el modelo, se utilizó el Índice de Precipitación Estandarizado (SPI) para identificar los eventos de sequía. Conclusiones— La implementación del modelo desarrollado puede permitir a las entidades gubernamentales tomar acciones para mitigar los impactos generados por sequías recurrentes en sus territorios. 17 páginas application/pdf eng Corporación Universidad de la Costa Colombia https://revistascientificas.cuc.edu.co/ingecuc/article/view/3812 Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia Modelo de inteligencia artificial y aprendizaje automático para la predicción espacial y temporal de eventos de sequía en el departamento del Magdalena, Colombia Artículo de revista http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 Text info:eu-repo/semantics/article http://purl.org/redcol/resource_type/ART info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 Colombia Magdalena INGE CUC [1] D. Wilhite, M. Sivakumar & D. Wood, Early warning systems for drought preparedness and drought management. GEN, CH: WMO, 2000. Available from http://www.wamis.org/agm/pubs/agm2/agm02.pdf [2] T. McKee, N. Doesken & J. Kleistet, “The relationship of drought frequency and duration to time scales,” presented at 8th Conference on Applied Climatology, ANA, CA, USA, 17-22 Jan. 1993. Available from https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf [3] WMO, “Índice normalizado de precipitación Guía de usuario”, GEN, CH: WMO, Report No. 1090, 2012. Available: https://library.wmo.int/doc_num.php?explnum_id=7769 [4] FAO, Sequía: FAO in Emergencies. [Online] Disponible en http://www.fao.org/emergencies/tipos-de-peligros-y-de-emergencias/sequia/es/ [Fecha de consulta 31 Ene. 2020]. [5] W. Cramer, G. Yohe, M. Auffhammer, C. Huggel, U. Molau, M. Dias, A. Solow, D. Stone, & L. Tibig, “Detection and attribution of observed impacts”, in Climate Change 2014: Impacts, Adaptation,and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the FifthAssessment Report of the Intergovernmental Panel on Climate Change, C. Field, V. Barros, D. Dokken, K. Mach, M. Mastrandrea, T. Bilir, M. Chatterjee, K. Ebi, Y. Estrada, R. Genova, B. Girma, E. Kissel, A. Levy, S. MacCracken, P. Mastrandrea & L. White, eds, CAMB, UK/NYC, NY, USA: Cambridge UP, 2014, pp. 979-1037. https://doi.org/10.1017/CBO9781107415379.023 [6] S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tig-nor & H. Miller, “Climate change 2007: the physical science basis,” Cambridge UP, CAMB, UK/NY, USA, Report, 2007. Available: https://www.ipcc.ch/report/ar4/wg1/ [7] A. Dai, T. Zhao & J. Chen, “Climate change and drought: A precipitation and evaporation perspective,” Curr Clim Change Rep, vol. 4, no. 9, pp. 301–312, May. 2018. https://doi.org/10.1007/s40641-018-0101-6 [8] S. Mukherjee, A. Mishra & K. Trenberth, “Climate change and drought: a perspective on drought indices,” Curr Clim Change Rep, vol. 4, no. 2, pp. 145–163, Jun. 2018. https://doi.org/10.1007/s40641-018- 0098-x [9] Revista SEMANA, “Las graves secuelas económicas de la sequía”, Semana, 23 Jul. 2014. [Online]. Disponible en https://www.semana.com/nacion/articulo/las-graves-secuelas-economicas-de-la-sequia/396750-3 [10] Redacción El Heraldo, “Magdalena, el más azotado por la temporada de sequía”, El Heraldo, 27 Sep. 2015. [Online]. Disponible en https://www.elheraldo.co/magdalena/magdalena-el-mas-azotado-por-latemporada-de-sequia-219590 [11] O. Rahmati, F. Falah, K. Dayal, R. Deo, F. Mohammadi, T. Biggs, D. Moghaddam, S. Naghibi & D. Bui, “Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia,” Sci Total Environ, vol. 699, pp. 1–10, Jan. 2020. https://doi.org/10.1016/j.scitotenv.2019.134230 [12] S. Park, J. Im, E. Jang & J. Rhee, “Drought assessment and monitoring through blending of multisensor indices using machine learning approaches for different climate regions,” Agric For Meteorol, vol. 216, pp. 157–169, Jan. 2016. https://doi.org/10.1016/j.agrformet.2015.10.011 [13] K. Fung, Y. Huang, C. Koo & M. Mirzaei, “Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia,” J Water Clim Chang, vol. 11, no. 4, pp. 1383–1398, Jun. 2019. https://doi.org/10.2166/wcc.2019.295 [14] P. Feng, B. Wang, D. Li Liu & Q. Yu, “Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in south-eastern Australia,” Agric. Syst, vol. 173, pp. 303–316, Aug. 2015. https://doi.org/10.1016/j.agsy.2019.03.015 [15] A. Belayneh, J. Adamowski, B. Khalil & J. Quilty, “Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction,” Atmos Res, vol. 172-173, pp. 37–47, Jun. 2016. https://doi.org/10.1016/j.atmosres.2015.12.017 [16] X. Liu, X. Zhu, Q. Zhang, T. Yang, Y. Pan & P. Sun, “A remote sensing and artificial neural networkbased integrated agricultural drought index: Index development and applications,” Catena, vol. 186, no. 2, pp. 1–10, Mar. 2020. https://doi.org/10.1016/j.catena.2019.104394 [17] J. Cruz & D. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer Inform, vol. 2, pp. 59–77, Dec. 2006. https://doi.org/10.1177/11769351060020003 [18] L. Deng & X. Li, “Machine learning paradigms for speech recognition: An overview,” IEEE Trans Audio Speech Lang Process, vol. 21, no. 5, pp. 1060–1089, May. 2013. https://doi.org/10.1109/ TASL.2013.2244083 [19] K. Rasouli, W. Hsieh & A. Cannon, “Daily streamflow forecasting by machine learning methods with weather and climate inputs,” J. Hydrol., vol. 414-415, pp. 284–293, Jan. 2012. https://doi.org/10.1016/j. jhydrol.2011.10.039 [20] D. Lary, A. Alavi, A. Gandomi & A. Walker, “Machine learning in geosciences and remote sensing,” GSF, vol.7, no. 1, pp. 3–10, Apr. 2015. https://doi.org/10.1016/j.gsf.2015.07.003 [21] Minambiente, UNGRD, IDEAM, Estrategia Nacional para la gestión integral de la sequía en Colombia. BO, CO: MinAmbiente, IDEAM, & UNGRD, 2018. Recuperado de https://www.unccd.int/sites/default/ files/country_profile_documents/ENGIS%2520para%2520publicaci%25C3%25B3n_Colombia.pdf [22] UNGRD, “Consolidado anual de emergencias”, (1998-2021), Gobierno de Colombia [Online]. Disponible en http://portal.gestiondelriesgo.gov.co/Paginas/Consolidado-Atencion-de-Emergencias.aspx (consultado 2020, May. 18). [23] Gobernación del Magdalena, “Nuestro departamento”, Gobierno de Colombia [Online]. Disponible en http://www.magdalena.gov.co/departamento/nuestro-departamento (consultado 2020, May. 18) [24] DANE, Resultados Censo Nacional de Población y Vivienda 2018. BO, CO: Gobierno de Colombia. Recuperado de https://www.dane.gov.co/files/censo2018/informacion-tecnica/presentaciones-territorio/191004- CNPV-presentacion-Magdalena.pdf [25] IDEAM, Magdalena. BO, CO: Gobierno de Colombia. Recuperado de http://atlas.ideam.gov.co/basefiles/ magdalena_texto.pdf [26] K. Trenberth, A. Dai, G. Van Der Schrier, P. Jones, J. Barichivich, K. Briffa & J. Sheffield, “Global warming and changes in drought,” Nat Clim Change, vol. 4, no. 1, pp. 17–22, Dec. 2013. https://doi. org/10.1038/nclimate2067 [27] N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau & R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sens Environ, vol. 202, pp. 18–27, Jul. 2016. https://doi.org/10.1016/j.rse.2017.06.031 [28] NASA. Normalized Difference Vegetation Index (NDVI), 30 Aug. 2000. Available: https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php [29] T. Du, D. Bui, M. Nguyen & H. Lee, “Satellite-based, multi-indices for evaluation of agricultural droughts in a highly dynamic tropical catchment, central Vietnam,” J Water, vol. 10, no. 5, pp. 1–24, Jan. 2018. https://doi.org/10.3390/w10050659 [30] L. Wang & J. Qu, “NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing,” Geophys Res Lett, vol. 34, no. 20, pp. 1–5, Jul. 2007. https://doi. org/10.1029/2007GL031021 [31] G. Poveda y O. Mesa, “Las fases extremas del fenómeno ENSO (El Niño y La Niña) y su influencia sobre la hidrología de Colombia”, Ing Hidraulic Mex, vol. 11, no. 1, pp. 21–37, Ene. 1996. Disponible en http:// www.revistatyca.org.mx/ojs/index.php/tyca/article/view/765 [32] D. Edwards & T. McKee, “Characteristics of 20th century drought in the United States at multiple time scales,” Dept. Atmos. Sci., CSU, FRT COLL., CO, USA, Climatology Report No. 97-2, Paper no. 634, 1997. Available: http://hdl.handle.net/10217/170176 [33] O. Valiente, “Sequía: definiciones, tipologías y métodos de cuantificación”, Invest Geogr, no. 26, pp. 59– 80, Mar. 2001. https://doi.org/10.14198/INGEO2001.26.06 [34] R. Mayorga y G. Hurtado, “La sequía en Colombia, Documento tecnico de respaldo a la informacion en la pagina web del IDEAM”, IDEAM, BO, CO, Report IDEAM–METEO/004-2006, 2006. Recuperado de http://www.cambioclimatico.gov.co/documents/21021/21147/NotaT%C3%A9cnicaSequia.pdf/d9ba4965- f7cd-4a2f-a875-2a38b1d6a941 [35] G. Hurtado, Sequía meteorológica y sequía agrícola en Colombia: Incidencia y tendencias. BO, CO: IDEAM, 2012. Disponible en http://www.ideam.gov.co/documents/21021/21138/Sequias+Incidencias+y+ Tendencias.pdf/3e72c86c-cf4a-42f9-95f1-07e7cf88861a [36] J. Gómez y M. Cadena, “Actualización de las estadísticas de la sequía en Colombia”, IDEAM, BO, CO, Nota técnica IDEAM-METEO/001-2018, Jun. 2017. Recupearado de http://www.ideam.gov.co/documents/21021/124446218/NT+001-2018_Actualizaci%C3%B3n+de+las+estad%C3%ADsticas+de+la+seq uia+en+Colombia/d47113b3-536b-4c83-a69c-22f97993016f?version=1.1 [37] NDMC, “Climographs,” SNR [Online]. Available: https://drought.unl.edu/Climographs.aspx (consultado: 2018, dec. 7). [38] W. Koehrsen, “A feature selection tool for machine learning in Python, Towards Data Science,” 22 Jun. 2018. Available: https://towardsdatascience.com/a-feature-selection-tool-for-machine-learning-inpython-b64dd23710f0 [39] C. Sutton, “11 - Classification and regression trees, bagging, and boosting,” Handb Stat, vol. 24, pp. 303–329, Dec. 2005. https://doi.org/10.1016/S0169-7161(04)24011-1 [40] E. Bauer & R. Kohavi, “An empirical comparison of voting classification algorithms: Bagging, boosting, and variants,” Mach Learn, vol. 36, no. 1-2, pp. 1–38, Jan. 1996. Available: http://robotics.stanford. edu/~ronnyk/vote.pdf [41] S. Safavian & D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans Syst Man Cybern, vol. 21, no. 3, pp. 660–674, Jun. 1991. https://doi.org/10.1109/21.97458 [42] M. Pal, “Random forest classifier for remote sensing classification,” Int J Remote Sens, vol. 26, no. 1, pp. 217–222, Oct. 2003. https://doi.org/10.1080/01431160412331269698 [43] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot & É. Duchesnay, “Scikit-learn: Machine learning in Python”, J Mach Learn Res, vol. 12, no. 85, pp. 2825–2830, Mar. 2011. Available: https://www.jmlr.org/papers/v12/pedregosa11a.html [44] Scikit-Learn, Scikit-Learn Machine Learning in Python [Online]. Available: https://scikit-learn.org/stable/index.html (consultado: 2020, May. 18). [45] W Fin de Semana, “Declaran calamidad pública por sequía en cinco municipios del Magdalena”, W Radio, 27 Jul. 2014. Disponible en https://www.wradio.com.co/noticias/actualidad/declaran-calamidadpublica-por-sequia-en-cinco-municipios-del-magdalena/20140727/nota/2341212.aspx [46] M. Correa, “La sequía impacta a 7 departamentos”, El Colombiano, 22 Jul. 2014. Disponible en https:// www.elcolombiano.com/historico/la_sequia_impacta_a_7_departamentos-IGEC_303649 265 249 2 18 Drought forecasting Standardized precipitation index Satellite imagery Google Earth Engine Machine learning Random forest Decision tree classifier Spatial interpolation Publicationopen.access