Czekanowsky Hypergraph-Based Deep Learning Classifier for Precision Cyclone Forecasting

Abstract

Early prediction of cyclones helps reduce deaths and damage to properties worldwide. With the advancement in satellite imaging technology, obtaining atmospheric images and remotely sensed objects such as cyclones is possible using different modalities. Such images are handy for weather prediction, specifically for forecasting cyclonic storms. However, it has a few limitations for achieving higher prediction accuracy with minimal time. A novel technique, proposed by Czekanowsky Dice Hypergraphic Extended Kalman Momentum Filterization based Bivariate Correlative Deep Structure Learning classification (CDHEKMF-BCDSLC), is introduced to achieve better cyclone prediction accuracy. Initially, the multiple satellite images are gathered from cyclone datasets - the proposed technique comprises four processing steps: segmentation, preprocessing, feature extraction, and classification. At first, Czekanowsky dices the Intensity threshold-based Interval Hypergraph (CDIT-IH) model using a Segmentation process. It minimized to perform cyclone prediction time. After that, preprocessing is applied to a novel invariant extended Kalman momentum filter designed to improve image contrast. Next, Bivariate correlative spatiotemporal feature extraction is performed on each image pixel intensity to extract features over time and location. Finally, a multidimensional deep belief network classification model is applied for accurate cyclone prediction. A multidimensional deep belief network classification model is a machine learning technique that consists of multiple layers for learning the given input (i.e., Spatiotemporal features). This process increases the prediction accuracy. Experimental results reveal that the proposed technique noticeably predicts cyclone conditional variants using prediction accuracy, precision, recall, F-measure, and prediction time concerning the number of cyclone images. The Quantitative results show that the proposed technique achieves 6% better accuracy with 17% minimum prediction time, 5% improved precision and f- f-measure, and 4% recall when compared to the state-of-the-art methods.