Hybrid optimized deep recurrent neural network for atmospheric and oceanic parameters prediction by feature fusion and data augmentation model

Sundeep Raj (), Sandesh Tripathi, K. C. Tripathi and Rajendra Kumar Bharti
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Sundeep Raj: VMSB Uttarakhand Technical University
Sandesh Tripathi: Birla Institute of Applied Sciences
K. C. Tripathi: Maharaja Agrasen Institute of Technology
Rajendra Kumar Bharti: BT Kumaon Institute of Technology

Journal of Combinatorial Optimization, 2024, vol. 47, issue 4, No 13, 33 pages

Abstract: Abstract In recent years climate prediction has obtained more attention to mitigate the impact of natural disasters caused by climatic variability. Efficient and effective climate prediction helps palliate negative consequences and allows favourable conditions for managing the resources optimally through proper planning. Due to the environmental, geopolitical and economic consequences, forecasting of atmospheric and oceanic parameters still results in a challenging task. An efficient prediction technique named Sea Lion Autoregressive Deer Hunting Optimization-based Deep Recurrent Neural Network (SLArDHO-based Deep RNN) is developed in this research to predict the oceanic and atmospheric parameters. The extraction of technical indicators makes the devised method create optimal and accurate prediction outcomes by employing the deep learning framework. The classifier uses more training samples and this can be generated by augmenting the data samples using the oversampling method. The atmospheric and the oceanic parameters are considered for the prediction strategy using the Deep RNN classifier. Here, the weights of the Deep RNN classifier are optimally tuned by the SLArDHO algorithm to find the best value based on the fitness function. The devised method obtains minimum mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE) of 0.020, 0.142, and 0.029 for the All India Rainfall Index (AIRI) dataset.

Keywords: Deep learning; Data augmentation; Technical indicators extraction; Brag–Curtis distance; Soil moisture (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10878-024-01159-1

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