A novel application of transformer neural network (TNN) for estimating pan evaporation rate
For decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challen...
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2024
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my.uniten.dspace-344262024-10-14T11:19:42Z A novel application of transformer neural network (TNN) for estimating pan evaporation rate Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. 36612762700 6506146119 57214837520 55807263900 Convolutional neural network Evaporation Long short-term memory Self-attention Transformer neural network Malaysia Benchmarking Brain Convolution Convolutional neural networks Crops Decision making Evaporation Farms Forecasting Irrigation Stochastic models Stochastic systems Water management Convolutional neural network Learning models Malaysians Neural-networks Novel applications Pan evaporation Predictive models Self-attention Transformer neural network Water loss artificial neural network data set evaporation machine learning numerical model prediction Long short-term memory For decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge in creating accurate and dependable predictive models is the evaporation procedure's non-stationarity, nonlinearity, and stochastic characteristics. This work examines, for the first time, a transformer-based deep learning architecture for evaporation prediction in four different Malaysian regions. The effectiveness of the proposed deep learning (DL) model, signified as TNN, is evaluated against two competitive reference DL models, namely Convolutional Neural Network and Long Short-Term Memory, and with regards to various statistical indices using the monthly-scale dataset collected from four Malaysian meteorological stations in the 2000�2019 period. Using a variety of input variable combinations, the impact of every meteorological data on the Ep forecast is also examined. The performance assessment metrics demonstrate that compared to the other benchmark frameworks examined in this work, the developed TNN technique was more precise in modelling monthly water loss owing to evaporation. In terms of predictive effectiveness, the proposed TNN model, enhanced with the self-attention mechanism, outperforms the benchmark models, demonstrating its potential use in the forecasting of evaporation. Relating to application, the predictive model created for Ep projection offers a precise estimate of water loss due to evaporation and can thus be used in irrigation management, agriculture planning based on irrigation, and the decrease in fiscal and economic losses in farming and related industries where consistent supervision and estimation of water are considered necessary for viable living and economy. � 2022, The Author(s). Final 2024-10-14T03:19:42Z 2024-10-14T03:19:42Z 2023 Article 10.1007/s13201-022-01834-w 2-s2.0-85145355729 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145355729&doi=10.1007%2fs13201-022-01834-w&partnerID=40&md5=5cf086cf05d1329919e10d544719f9cc https://irepository.uniten.edu.my/handle/123456789/34426 13 2 31 All Open Access Gold Open Access Springer Science and Business Media Deutschland GmbH Scopus |
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Convolutional neural network Evaporation Long short-term memory Self-attention Transformer neural network Malaysia Benchmarking Brain Convolution Convolutional neural networks Crops Decision making Evaporation Farms Forecasting Irrigation Stochastic models Stochastic systems Water management Convolutional neural network Learning models Malaysians Neural-networks Novel applications Pan evaporation Predictive models Self-attention Transformer neural network Water loss artificial neural network data set evaporation machine learning numerical model prediction Long short-term memory |
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Convolutional neural network Evaporation Long short-term memory Self-attention Transformer neural network Malaysia Benchmarking Brain Convolution Convolutional neural networks Crops Decision making Evaporation Farms Forecasting Irrigation Stochastic models Stochastic systems Water management Convolutional neural network Learning models Malaysians Neural-networks Novel applications Pan evaporation Predictive models Self-attention Transformer neural network Water loss artificial neural network data set evaporation machine learning numerical model prediction Long short-term memory Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| description |
For decision-making in farming, the operation of dams and irrigation systems, as well as other fields of water resource management and hydrology, evaporation, as a key activity throughout the universal hydrological processes, entails efficient techniques for measuring its variation. The main challenge in creating accurate and dependable predictive models is the evaporation procedure's non-stationarity, nonlinearity, and stochastic characteristics. This work examines, for the first time, a transformer-based deep learning architecture for evaporation prediction in four different Malaysian regions. The effectiveness of the proposed deep learning (DL) model, signified as TNN, is evaluated against two competitive reference DL models, namely Convolutional Neural Network and Long Short-Term Memory, and with regards to various statistical indices using the monthly-scale dataset collected from four Malaysian meteorological stations in the 2000�2019 period. Using a variety of input variable combinations, the impact of every meteorological data on the Ep forecast is also examined. The performance assessment metrics demonstrate that compared to the other benchmark frameworks examined in this work, the developed TNN technique was more precise in modelling monthly water loss owing to evaporation. In terms of predictive effectiveness, the proposed TNN model, enhanced with the self-attention mechanism, outperforms the benchmark models, demonstrating its potential use in the forecasting of evaporation. Relating to application, the predictive model created for Ep projection offers a precise estimate of water loss due to evaporation and can thus be used in irrigation management, agriculture planning based on irrigation, and the decrease in fiscal and economic losses in farming and related industries where consistent supervision and estimation of water are considered necessary for viable living and economy. � 2022, The Author(s). |
| author2 |
36612762700 |
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36612762700 Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. |
| format |
Article |
| author |
Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. |
| author_sort |
Abed M. |
| title |
A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| title_short |
A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| title_full |
A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| title_fullStr |
A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| title_full_unstemmed |
A novel application of transformer neural network (TNN) for estimating pan evaporation rate |
| title_sort |
novel application of transformer neural network (tnn) for estimating pan evaporation rate |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2024 |
| _version_ |
1814061180350627840 |
| score |
13.222552 |