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The satellite images are more attracted in the field of flood detection. For planning actions during emergencies, flood detection plays a vital role, but the major barrier is that using satellite images to detect flooded regions. For flood detection, this method innovates a model named Whale-crow search algorithm on the basis of deep convolutional neural network (W-CSA DCNN) approach. Pre-processing, classification, segmentation and feature extraction are the four steps which is included in this model. For obtaining sound and antiquity from the input image initially, the satellite imagery is given to pre-processing and then for obtaining the features on the basis of vegetation indices the pre-processed image is put through the feature extraction process. By means of Kernel Fuzzy Auto regressive (KFAR) model, the acquire features are subsequently used in the segmentation process. After obtaining the segments, it is given to the classification, which is carried out by means of DCNN and qualified excellently via the W-CSA that is the combination of the Crow Search Algorithm (CSA) and Whale optimisation algorithm (WOA). Based on the specificity, accuracy and sensitivity with values 0.982, 0.972 and 0.975, the efficiency of this process deliberates advanced performance than the existing process.

Whale- crow search optimisation enabled deep convolutional neural network for flood detection