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Hybrid deep learning method for a week-ahead evapotranspiration forecasting

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Abstract

Reference crop evapotranspiration (ETo) is an integral hydrological factor in soil–plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning approach, combining convolutional neural network (CNN) and gated recurrent unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ETo forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived moderate resolution imaging spectroradiometer, ground-based datasets from scientific information for landowners and synoptic-scale climate indices. To develop a vigorous CNN-GRU model, a feature selection stage entails the ant colony optimization method implemented to improve the ETo forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ETo.

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Abbreviations

AA:

Advection–Aridity

ACO:

Ant colony optimization

ANN:

Artificial neural network

AO:

Arctic oscillation

BOM:

Australian bureau of meteorology

CCF:

Cross correction function

CNN:

Convolutional neural network

CNN-GRU:

Hybrid model integrating the ACO and CNN algorithm with GRU

CNN-LSTM:

Hybrid model integrating the ACO and CNN algorithm with LSTM

CI:

Climate indices

DL:

Deep learning

DMI:

Dipole model index

ECDF:

Empirical cumulative distribution function

ELM:

Extreme learning machine

EMD:

Empirical mode decomposition

EMI:

El-Nino southern oscillation Modoki index

ENSO:

El Niño Southern oscillation

ETo :

Reference crop evapotranspiration

FAO:

Food and agriculture organization

FE:

Forecasting error

FFNN:

Feed forward neural networks

GB:

Giga bite

GCM:

Global climate model

GRU:

Gated recurrent unit

GIOVANNI:

Geospatial online interactive visualization and analysis infrastructure

KNMI:

Royal Netherlands meteorological institute

LM:

Legates-McCabe's index

LSTM:

Long- short term memory

LS-SVM:

Least-squares support vector machines

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MARS:

Multivariate adaptive regression splines

MDB:

Murray-Darling basin

ML:

Machine learning

MLP:

Multi-layer perceptron

MLR:

Multilinear regression

MJO:

Madden–Julian oscillation

MODIS:

Moderate resolution imaging spectroradiometer

MSE:

Mean squared error

NOAA:

National oceanic and atmospheric administration

NSE:

Nash–sutcliffe efficiency

PACF:

Partial autocorrelation function

PM:

Penman–Monteith

PT–FC:

Priestley–Taylor and Flint–Childs

R:

Correlation coefficient

RF:

Random forest

RMSE:

Root-mean-square-error

RNN:

Recurrent neural network

RRMSE:

Relative root-mean-square error

SD:

Standard deviation

SAM:

Southern annular mode

SILO:

Scientific information for landowners

SOI:

Southern oscillation index

SST:

Sea surface temperature

SVR:

Support vector regression

SW:

Shuttleworth–Wallace

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Acknowledgements

The research was supported by the Chinese Academy of Science (CAS) and the University of Southern Queensland (USQ) under the USQ-CAS Postgraduate Research Scholarship (2019–2021). Data were obtained from MODIS-satellite, SILO, and NOAA databases, which are duly acknowledged. We also thank the Editor and Reviewers for their insightful comments.

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Authors and Affiliations

  1. School of Sciences, University of Southern Queensland, Springfield, QLD, 4300, Australia

    A. A. Masrur Ahmed, Ravinesh C. Deo & Nawin Raj

  2. Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, Beijing, China

    Qi Feng, Zhenliang Yin & Linshan Yang

  3. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd 320, LanzhouGansu Province, 730000, China

    Qi Feng, Zhenliang Yin & Linshan Yang

  4. Centre for Sustainable Agricultural Systems, University of Southern Queensland, Springfield, QLD, 4500, Australia

    Afshin Ghahramani

Authors
  1. A. A. Masrur Ahmed
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  2. Ravinesh C. Deo
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  3. Qi Feng
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  4. Afshin Ghahramani
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  7. Linshan Yang
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Contributions

A. A. Masrur Ahmed: Writing—original draft, Conceptualization, Methodology, Software, Model development, and application. Ravinesh C. Deo: Conceptualization, Writing—review & editing, Investigation, Supervision. Afshin Ghahramani: Writing—review & editing. Nawin Raj: Writing—review & editing, Qi Feng: Writing—review & editing, Zhenliang Yin: Writing—review & editing, Linshan Yang: Writing—review & editing.

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Correspondence to A. A. Masrur Ahmed or Ravinesh C. Deo.

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Ahmed, A.A.M., Deo, R.C., Feng, Q. et al. Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stoch Environ Res Risk Assess 36, 831–849 (2022). https://doi.org/10.1007/s00477-021-02078-x

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