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 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.

• 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
• Rights: openAccess
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)