Discriminant Granger and Camargo Index Jensen Shannon Boosting Classifier for Enhanced Marine Weather Forecasting
Discriminant Granger and Camargo Index Jensen Shannon Boosting Classifier for Enhanced Marine Weather Forecasting |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Soumya Unnikrishnan, Smitha Vinod | ||
| DOI : 10.14445/22315381/IJETT-V73I7P138 | ||
How to Cite?
Soumya Unnikrishnan, Smitha Vinod, "Discriminant Granger and Camargo Index Jensen Shannon Boosting Classifier for Enhanced Marine Weather Forecasting," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.493-504, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P138
Abstract
Predicting the weather is essential for people’s everyday demands and tasks. Furthermore, a number of industries, like agriculture, irrigation, etc., depend on precise weather forecasting. Many people may experience issues as a result of weather forecast deviations. Therefore, making accurate weather predictions is a crucial issue that requires attention. Enormous data assessment is a process for looking into enormous data in order to find hidden patterns that may lead to improved results. Big data has gained attention in a number of societal sectors in recent years. The evaluation of big data helps forecasters make more accurate weather predictions and produces better results while doing so. Therefore, reliable weather prediction is beyond the capabilities of standard computer intelligence models. The ceaseless evolution of big data technology necessitates preferable learning methods to discover the data value. However, the prevailing divergence and redundancy in the data acquired from a series of buoys make it both laborious and cumbersome to accurately predict future information. Motivated by these challenges, an ensemble paradigm called Discriminant Granger Causality and Camargo-Index Jensen Shannon Boosting Classifier (DGC-CJSBC) is proposed for enhanced marine weather forecasting. Here, enhanced refers to the marine weather forecasting employing big marine data using a boosting classifier model. The DGC-CJSBC method for enhanced marine weather forecasting is split into two sections. First, a linear combination of features characterizing two classes is modeled. DGC-CJSBC method deploys a novel change detection (i.e., changes observed between previous day oceanographic and surface meteorological readings) based on Jensen–Shannon divergence to record changes throughout the equatorial Pacific. Moreover, the DGC-CJSBC method considers the Camargo Evenness Index Quadratic function. In this way, classification performance improved. The El Nino Big dataset was applied to train the proposed model. Contrary to conventional algorithms, the DGC-CJSBC method outcome offers better accuracy, time, error and space complexity.
Keywords
Big marine data, Discriminant, Granger causality, Camargo-index, Jensen shannon, Boosting classifier.
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