Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia
Fuente:
Redalyc
Saved in:
| Main Author: | |
|---|---|
| Format: | Artículo científico |
| Language: | en |
| Published: |
Corporación Universitaria de la Costa
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1876457688317034496 |
|---|---|
| author | Daissy Milenys Herrera Posada |
| author_facet | Daissy Milenys Herrera Posada |
| contents | Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia Daissy Milenys Herrera Posada Edier Aristizábal Ingeniería random forest machine learning satellite imagery Drought forecasting Google Earth Engine 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. 2022 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779342003 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.2 Vol.18 |
| format | Artículo científico |
| id | redalyc_497779342003 |
| institution | Redalyc |
| language | en |
| publishDate | 2022 |
| publisher | Corporación Universitaria de la Costa |
| spellingShingle | Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia Daissy Milenys Herrera Posada Ingeniería random forest machine learning satellite imagery Drought forecasting Google Earth Engine Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia Daissy Milenys Herrera Posada Edier Aristizábal Ingeniería random forest machine learning satellite imagery Drought forecasting Google Earth Engine 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. 2022 artículo científico 0122-6517 https://www.redalyc.org/articulo.oa?id=497779342003 en http://www.redalyc.org/revista.oa?id=4977 INGE CUC application/pdf Corporación Universitaria de la Costa INGE CUC (Colombia) Num.2 Vol.18 |
| title | Artificial Intelligence and Machine Learning Model for Spatial and Temporal Prediction of Drought Events in the Department of Magdalena, Colombia |
| topic | Ingeniería random forest machine learning satellite imagery Drought forecasting Google Earth Engine |
| url | https://www.redalyc.org/articulo.oa?id=497779342003 |