Abstract
In the recent years, there are many extreme climate changes and atmospheric changes are occurring around us. There is a huge rise in the global temperature due to global warming, CO2 and many reasons. Forecasting them in advance could prevent the consequences and also to take preventive measures. There are enormous numerical models and algorithms that have been developed and imposed to predict the weather forecasting. However, accuracy is a great component for weather predication. As there are many weather components available, the appropriate parameter based on the forecasting region must be selected. The proposed work considers the inter-dependencies between the weather parameters for weather forecasting with better accuracy. The multivariate regression analysis is performed for identifying appropriate parameters and their dependency. The experimental results show that the appropriate identification of required climatic parameters improves the accuracy in weather forecasting.
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Kalyani, V., Valliyammai, C., Manoj, S. (2020). Multivariate Regression Analysis on Climate Variables for Weather Forecasting in Indian Subcontinent. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_57
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DOI: https://doi.org/10.1007/978-981-15-2475-2_57
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