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IRF-LSTM: enhanced regularization function in LSTM to predict the rainfall

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Abstract

Sea surface temperature modifies the air in the earth’s atmosphere. \({\text{EINi}}\tilde{\eta }{\text{o}}\) signs in the sea surface temperature spread the warm water in the ocean and take the rain. Several existing models forecast rainfall using statistical analysis, machine learning, and deep learning. Neural Networks are powerful models that are extensively used for solving problems in many areas but still suffer from significant weaknesses. The presence of nonlinear hidden layers makes the deep networks prone to severe overfitting. This research used a rainfall dataset and reduced the issue of overfitting by using the improved regularized function. The main contribution of this research is proposing the enhanced regularized function to predict rainfall to reduce the bias. To evaluate the performance of the proposed Improved Regularization Function Long Short-Term Memory (IRF-LSTM) to forecast rainfall are RMSE, MAE, NSE, r. The output performance of the proposed IRF-LSTM is surpassed other state-of-the-art methods and is verified as by far the best implementation.

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Abbreviations

IPCC:

Intergovernmental panel on climate change

NECP:

National Centers for Environmental Prediction

NWS:

National weather service

SVR:

Support vector regression

NOOA:

National oceanic and atmospheric administration

RNN:

Recurrent neural networks

SVM:

Support vector machine

LSTM:

Long short-term memory

CNN:

Convolution neural network

MLP:

Multi-layer perceptron

ANN:

Artificial neural network

APSDPS:

Andhra Pradesh State Development Planning Society

GRU:

Gated recurrent unit

RMSE:

Root mean square error

MAE:

Mean absolute error

NSE:

Nash Sutcliffe model efficiency

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Authors and Affiliations

  1. School of Competitive Coding (SOCC), Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Usharani Bhimavarapu

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  1. Usharani Bhimavarapu
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Correspondence to Usharani Bhimavarapu.

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Bhimavarapu, U. IRF-LSTM: enhanced regularization function in LSTM to predict the rainfall. Neural Comput & Applic 34, 20165–20177 (2022). https://doi.org/10.1007/s00521-022-07577-8

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  • DOI: https://doi.org/10.1007/s00521-022-07577-8

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