A Novel Representation and Prediction Initiative for Underground Water by Using Deep Learning Technique of Remote Sensing Images

Abstract

This paper intends to introduce a novel groundwater prediction model by inducing the novel hydro indices that are not yet popular in earlier techniques. As per the proposed work, statistical features like mean, median, skewness and kurtosis are estimated. Moreover, the vegetation index includes simple ratio, normalized difference vegetation index, Kauth–Thomas Tasseled cap transformation and infrared index transformation. Furthermore, a novel hydro index is formulated by combining the statistical model function with the vegetation index. Subsequently, the detection process is carried out by ensemble technique, which includes the classifiers like random forest (RF), neural network (NN), support vector machine (SVM) and deep belief network (DBN). The final predicted result is attained from DBN. The performance of the adopted model is computed to the existing models with respect to certain measures. At learning rate 50, the maximum accuracy of the proposed model is 45.65, 34.78, 58.70, 72.83, 18.48 and 23.91% better than the existing models like SVM, RF, convolutional neural network, K-nearest neighbors, NN and artificial neural network, respectively.