Evaluation of deep learning approaches for classification of drought stages using satellite imagery for Tharparker

Droughts have grown increasingly common, severe, and widespread in recent decades due to climate change, aggravating their harmful repercussions. Drought prediction is very effective for providing early warning and protecting the most susceptible areas from the dangers of drought. This study looked at the feasibility of applying Deep Neural Networks to create drought stage classification models for the Tharpakar District of Pakistan. A collection of satellite pictures of Tharpakar at various degrees of the drought were employed in this investigation. The unique dataset utilized in this study was gathered utilizing the time-lapse function of Google Earth Pro. The drought stages considered in this study are 'Before Drought,' 'Drought,' 'After Drought,' and 'No Drought.' DenseNet, ResNet, InceptionV3, Xception, and VGG19 deep learning architectures were utilized for training the models. Accuracy, Precision, Recall, F1-Score, and ROC curves were used to evaluate all models. According to the experimental results, DenseNet and ResNet were the best-performing models with an accuracy of 70%, while VGG19 was the lowest-performing model with an accuracy of 60%.