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

Sarmad Dashti Latif ()
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Sarmad Dashti Latif: Komar University of Science and Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 5, No 32, 11729-11742

Abstract: 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.

Keywords: Pan evaporation; Deep learning; Machine learning; Forecasting model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03469-6

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