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ILF-LSTM: enhanced loss function in LSTM to predict the sea surface temperature

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Abstract

Globe's primary issue is global warming, water temperatures have accompanied it as the sea surface temperature, and it is the primary attribute to balance the energy on the earth's surface. Sea surface temperature prediction is vital to climate forecast. Downwelling currents carry some of this heat to the ocean's bottom layers, which are also heating, covering far behind the increase in sea surface temperature. In deep learning models, the correct loss function will try to reduce the error and converge fast. The proposed improved loss function correctly estimates how close the predictions made by the long short-term memory match the observed values in the training data. This research considers location-specific sea surface temperature predictions using the improved loss function in the long short-term memory neural network at six different locations around India for daily, weekly, and monthly time horizons. Most existing research concentrated on periodic forecasts, but this paper focused on daily, weekly, and monthly predictions. The improved loss function—long short-term memory, achieved 98.7% accuracy, and this improved loss function overcomes the limitations of the existing techniques and reduces the processing time to ~ 0.35 s. In this research, the sea surface temperature prediction using the improved loss function in the long short-term memory neural network gives better results than the standard prediction models and other existing techniques by considering the long-time dependencies and obtaining features from the spatial data.

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Abbreviations

MIMO:

Multiple input multiple output

CEEMD-BPNN:

Complementary ensemble empirical mode decomposition-backpropagation neural network

EEMD-BPNN:

Ensemble empirical mode decomposition-backpropagation neural network

SVR:

Support vector regression

ROMS:

Regional Ocean Modeling System

CFCC-LSTM-RNN:

Combined fully connected convolutional long short-term memory recurrent neural networks

SVM:

Support vector machine

FC-LSTM:

Fully connected long short-term memory

SOM:

Self-organizing map

MLPR:

Multilayer perceptron regression

DANN:

Dimensional reduction analysis neural network

OLS:

Ordinary least square

NOAA:

National oceanic and atmospheric administration

IPCC:

Intergovernmental Panel on Climate Change

OISST:

Optimum Interpolation Sea Surface Temperature

HadISST:

Hadley center sea ice and sea surface temperature

SST:

Sea surface temperature

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This research received no external funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Bhimavarapu Usharani

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  1. Bhimavarapu Usharani
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Correspondence to Bhimavarapu Usharani.

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Communicated by Oscar Castillo.

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Usharani, B. ILF-LSTM: enhanced loss function in LSTM to predict the sea surface temperature. Soft Comput 27, 13129–13141 (2023). https://doi.org/10.1007/s00500-022-06899-y

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