Abstract
Evapotranspiration is one of the most critical features in hydrology. In order to address the prediction of evapotranspiration accurately and scientifically, this study proposes a novel deep learning-based evapotranspiration prediction model, the CNN-BiLSTM-Attention model, using the climatically complex Mount Tai region in China as a case study. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN), the temporal dependency capturing of Bidirectional Long Short-Term Memory Networks (BiLSTM), and the feature weighting abilities of the Attention mechanism. In order to enhance prediction accuracy with fewer climate parameters, various input parameter combinations are explored and compared with other classical models in this study. The model's performance is assessed across daily, weekly, and monthly time increments, using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). This study holds significant implications for mountain hydrological cycles, as accurate evapotranspiration prediction aids in more informed decision-making for agricultural production, water resource management, and climate change research.
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Funding
This study was funded by National Natural Science Foundation of China (42002282).
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Shichao Wang designed and conducted the experiments, analyzed the results, and wrote the initial draft of the paper. Xiaoge Yu assisted with the revision of the initial draft and participated in data analysis. Yan Li provided the data and also participated in data analysis. Shujun Wang conducted the final review and revision of the paper. Can Meng contributed to editing and visualization.
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In the course of this study, we have adhered to the highest ethical standards. We affirm that the research does not involve any breach of data privacy. The climate data used in this study was either provided by the corresponding author's affiliated institution or sourced from publicly available datasets, and it does not contain any personal or confidential information. Furthermore, we have taken appropriate measures to ensure that the training data used in our analysis is unbiased and representative of the Mount Tai region's climate patterns.
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Communicated by: Hassan Babaie
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Wang, S., Yu, X., Li, Y. et al. Application of a hybrid deep learning approach with attention mechanism for evapotranspiration prediction: a case study from the Mount Tai region, China. Earth Sci Inform 16, 3469–3487 (2023). https://doi.org/10.1007/s12145-023-01103-7
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DOI: https://doi.org/10.1007/s12145-023-01103-7