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.
For the humanity to the whole and all the creatures of this world, the underground water is the greatest resource to rely upon that highly forms an indispensable factor toward augmented livelihood. In spite of the lack of detailed knowledge, global warming is found to profoundly influence underground water resources through changes in underground water recharge. Prediction of the underground water under a changing climate is essential to living beings. In this article, underground water prediction using optimal deep neural networks (optimal DNN) has been attempted. Initially, the features of temperature and rainfall among the input data have been selected and after which, the chosen data have been fed to the DNN to predict the underground water. In DNN, weight parameters are optimally selected with the help of fish swarm optimization (FSO). The implementation has been done on MATLAB. The simulation results found that the proposed FSO-DNN prediction approach outperforms the existing prediction approaches by 78.9% accuracy, 83% sensitivity, 88% specificity, 95.8% positive predictive values, 52.3% negative predictive values, and 95.8% F-measure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.