Abstract
Rainfall prediction plays a major role in ensuring the livelihood of many people especially, for the farmers. Heavy and irregular flow of rainfall can cause flood, landslide and much other destruction. To prevent this, rainfall should be predicted in a periodic manner. As a contribution, the proposed Spotted Hyena based nonlinear autoregressive model (SH-NARX) prediction model effectively predicts the rainfall in a yearly, monthly and quarterly manner using the Indian rainfall dataset. The data is collected and trained using the NARX neural network, which is a non linear autoregressive network that is optimized using the spotted hyena optimization for rainfall prediction. The performance of the prediction model is analyzed based on RMSE and PRD that are minimal, highlighting the higher accuracy rates.
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Mukkala, A.R.K., Reddy, S.S.S., Raju, P.P., Mounica, Oguri, C., Bhukya, S. (2022). Optimization Enabled Neural Network for the Rainfall Prediction in India. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_2
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DOI: https://doi.org/10.1007/978-3-031-12641-3_2
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