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.
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Abbreviations
- LSTM:
-
Long short-term memory
- MLP-NN:
-
Multilayer perceptron neural network
- ACF:
-
Autocorrelation function
- RMSE:
-
Root mean square error
- MAE:
-
Mean absolute error
- R 2 :
-
Coefficient of determination
- ANN:
-
Artificial neural network
- MLP:
-
Multilayer perceptron
- RBF:
-
Radial basis function
- ANFIS:
-
Adaptive neuro-fuzzy interface system
- ANFIS-SA:
-
Adaptive neuro-fuzzy interface system-shark algorithm
- SVR:
-
Support vector regression
- CART:
-
Classification and regression tree
- CCNNs:
-
Cascade correlation neural networks
- GEP:
-
Gene expression programming
- ELR:
-
ElasticNet linear regression
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The author would like to thank Australian Government for providing data through the Bureau of Meteorology.
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Latif, S.D. Evaluating deep learning and machine learning algorithms for forecasting daily pan evaporation during COVID-19 pandemic. Environ Dev Sustain 26, 11729–11742 (2024). https://doi.org/10.1007/s10668-023-03469-6
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DOI: https://doi.org/10.1007/s10668-023-03469-6