首页|Self-attention transformer model for pan evaporation prediction: a case study in Australia

Self-attention transformer model for pan evaporation prediction: a case study in Australia

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  • NETL
  • NSTL
  • Iwa Publishing
In drought-prone regions like Australia, accurately assessing evaporation rates is essential for effectively managing and maximising the use of precious water resources and reservoirs. Current estimates show that evaporation reduces Australia's open water lake capacity by about 40% annually. With climate change, this water loss is expected to become an even greater concern. This study investigates a transformer-based neural network (TNN) to estimate monthly evaporation in three Australian locations. The models were trained and tested using monthly weather data spanning from 2009 to 2022. Input parameters were chosen based on Pearson's correlation coefficient values to identify the most impactful combinations. The developed TNN model was compared with two widely used empirical methods, namely Thornthwaite and Stephens and Stewart. The TNN model's impressive accuracy in evaporation prediction, attributed to its unique self-attention mechanism, suggests its promising potential for future use in evaporation forecasting. Additionally, the study revealed an intriguing result: Despite using the same input datasets, the TNN model surpassed traditional methods, achieving an average improvement of 18% in prediction accuracy. The TNN prediction model accurately predicts water loss (average R~2 = 0.970), supports irrigation management and agricultural planning and offers financial benefits to farming and related industries.

evaporationself-attentionStephens and Stewart modelThornthwaite modeltransformer model

Mustafa Abed、Monzur Alam lmteaz、Yuk Feng Huang、Ali Najah Ahmed

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Department of Civil and Construction Engineering, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia

Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia

Research Centre For Human-Machine Collaboration (HUMAC), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia||Department of Engineering, School of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya 47500, Malaysia

2024

10.2166/hydro.2024.104

Journal of hydroinformatics

Journal of hydroinformatics

SCI
ISSN：1464-7141
年,卷(期)：2024.26(10/12)
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