Intelligent rainfall forecasting model: heuristic assisted adaptive deep temporal convolutional network with optimal feature selection
by Nishant Nilkanth Pachpor; B. Suresh Kumar; Prakash S. Prasad; Salim G. Shaikh
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 16, No. 4, 2024

Abstract: A deep learning technology is adopted to predict seasonal rainfall efficiently. Various rainfall data are collected from the internet. A deep feature extraction is done by autoencoder. Further, the deep extracted features are provided to the optimal feature selection phase, where the weights are optimised by utilising the developed modified attack power-based sail fish-hybrid leader optimisation (MAP-SFHLO). Then, the selected optimal features are provided as input to the prediction stage, and the prediction is done using the enhanced atrous-based adaptive deep temporal convolutional network (EA-ADTCN) along with the aid of the developed MAP-SFHLO algorithm to offer an effective prediction rate as the final outcome. Throughout the analysis, the performance of the developed model shows 5.2% and 6.0% regarding MAE and RMSE metrics. Thus, the suggested system performs more accurately in terms of accuracy rate in predicting rainfall than conventional techniques.

Online publication date: Tue, 01-Oct-2024

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