Hybrid deep learning method for a week-ahead evapotranspiration forecasting

Ahmed, A. A. Masrur and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Feng, Qi and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Raj, Nawin and Yin, Zhenliang and Yang, Linshan (2021) Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stochastic Environmental Research and Risk Assessment. ISSN 1436-3240


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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 7 September 2021. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher. This research was supported by USQ-CAS Postgraduate Research Scholarship 2019 - 2021 under project leader Professor Ravinesh Deo and Professor Qi Feng (Chinese Academy of Sciences, CAS).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 09 Sep 2021 01:28
Last Modified: 09 Sep 2021 01:28
Uncontrolled Keywords: convolutional neural network; gated recurrent unit; hybrid-deep learning; ETo forecasting
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Funding Details:
Identification Number or DOI: https://doi.org/10.1007/s00477-021-02078-x
URI: http://eprints.usq.edu.au/id/eprint/43525

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