Drought Spatial Object Prediction Approach using Artificial Neural Network

Drought Spatial Object Prediction Approach using Artificial Neural Network

The concept of object identification and modeling has fueled a lengthy scientific effort to convert remotely sensed images into geographic phenomena. The objective of this article was to develop a new concept for characterizing and identifying drought spatial objects from satellite images for improved drought prediction and mitigation using a back propagation artificial neural network (ANN). To characterize drought as a spatial object, 11 attributes from multi-sensors and resolutions (such as Standardized Deviation of Normalized Difference Vegetation Index [SDNDVI], Digital Elevation Model [DEM], Soil Water Holding Capacity, Ecological Regions, Land Cover, Standard Precipitation Index [SPI], and oceanic indices were used.