Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia

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...

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Autores:
Herrera Posada, Daissy Milenys
Aristizábal, Edier
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10017
Acceso en línea:
https://hdl.handle.net/11323/10017
https://repositorio.cuc.edu.co/
Palabra clave:
Drought forecasting
Standardized precipitation index
Satellite imagery
Google Earth Engine
Machine learning
Random forest
Decision tree classifier
Spatial interpolation
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openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
id RCUC2_103a149b9c751ddf6c97120758a06d12
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10017
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
dc.title.translated.none.fl_str_mv Modelo de inteligencia artificial y aprendizaje automático para la predicción espacial y temporal de eventos de sequía en el departamento del Magdalena, Colombia
title Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
spellingShingle Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
Drought forecasting
Standardized precipitation index
Satellite imagery
Google Earth Engine
Machine learning
Random forest
Decision tree classifier
Spatial interpolation
title_short Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
title_full Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
title_fullStr Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
title_full_unstemmed Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
title_sort Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, Colombia
dc.creator.fl_str_mv Herrera Posada, Daissy Milenys
Aristizábal, Edier
dc.contributor.author.none.fl_str_mv Herrera Posada, Daissy Milenys
Aristizábal, Edier
dc.subject.proposal.eng.fl_str_mv Drought forecasting
Standardized precipitation index
Satellite imagery
Google Earth Engine
Machine learning
Random forest
Decision tree classifier
Spatial interpolation
topic Drought forecasting
Standardized precipitation index
Satellite imagery
Google Earth Engine
Machine learning
Random forest
Decision tree classifier
Spatial interpolation
description 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.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-04-25T17:02:28Z
dc.date.available.none.fl_str_mv 2023-04-25T17:02:28Z
dc.type.spa.fl_str_mv Artículo de revista
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http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
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dc.identifier.citation.spa.fl_str_mv 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
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dc.identifier.eissn.spa.fl_str_mv 2382-4700
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 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
10.17981/ingecuc.18.2.2022.20
2382-4700
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10017
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv INGE CUC
dc.relation.references.spa.fl_str_mv [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
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Derechos de autor 2022 INGE CUChttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Herrera Posada, Daissy MilenysAristizábal, Edier2023-04-25T17:02:28Z2023-04-25T17:02:28Z2022D. 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.18.2.2022.200122-6517https://hdl.handle.net/11323/1001710.17981/ingecuc.18.2.2022.202382-4700Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://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 páginasapplication/pdfengCorporación Universidad de la CostaColombiahttps://revistascientificas.cuc.edu.co/ingecuc/article/view/3812Artificial intelligence and machine learning model for spatial and temporal prediction of drought events in the department of Magdalena, ColombiaModelo de inteligencia artificial y aprendizaje automático para la predicción espacial y temporal de eventos de sequía en el departamento del Magdalena, ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85ColombiaMagdalenaINGE 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_303649265249218Drought forecastingStandardized precipitation indexSatellite imageryGoogle Earth EngineMachine learningRandom forestDecision tree classifierSpatial interpolationPublicationORIGINALArtificial Intelligence and Machine Learning Model.pdfArtificial Intelligence and Machine Learning Model.pdfArtículoapplication/pdf2079077https://repositorio.cuc.edu.co/bitstreams/2bfe2cf4-dd1b-4658-812d-c1f9a7972631/downloade17a4871b05e652a7bdcf1bbbade792bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/67b26c6c-1495-4379-bb33-2f00f3806590/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTArtificial Intelligence and Machine Learning Model.pdf.txtArtificial Intelligence and Machine Learning Model.pdf.txtExtracted 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
