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Evaluating deep learning and machine learning algorithms for forecasting daily pan evaporation during COVID-19 pandemic

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  • Sarmad Dashti Latif

    (Komar University of Science and Technology)

Abstract

In this study, a deep learning algorithm namely long short-term memory (LSTM) has been developed for forecasting daily pan evaporation at Sydney airport, Australia. The accuracy of the developed LSTM model has been compared with a commonly used machine learning model, namely multilayer perceptron neural network (MLP-NN). The evaporation rate as a single parameter was used with one time-lag based on autocorrelation function (ACF). The utilized data duration was from January 2021 to February 2022 (during Covid-19 pandemic). Different statistical measurements have been applied in order to evaluate the performance of the proposed models. The results showed that the developed LSTM model outperformed MLP-NN. The LSTM performed well with RMSE = 1.074, MAE = 0.771, R2 = 0.97, while the MLP-NN had least performance with RMSE = 2.801, MAE = 1.994, and R2 = 0.57. The developed LSTM model could be utilized in other locations for forecasting daily pan evaporation.

Suggested Citation

  • Sarmad Dashti Latif, 2024. "Evaluating deep learning and machine learning algorithms for forecasting daily pan evaporation during COVID-19 pandemic," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 11729-11742, May.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:5:d:10.1007_s10668-023-03469-6
    DOI: 10.1007/s10668-023-03469-6
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    References listed on IDEAS

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    1. Bowyer, Dorothea & Chapman, Ross L., 2014. "Does privatisation drive innovation? Business model innovation through stakeholder viewpoints: the case of Sydney Airport 10 years post-privatisation," Journal of Management & Organization, Cambridge University Press, vol. 20(3), pages 365-386, May.
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