Jayasinghe, W. J. M. Lakmini Prarthana and Deo, Ravinesh C. 
ORCID: https://orcid.org/0000-0002-2290-6749 and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Ghimire, Sujan and Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644
(2021)
Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm.
IEEE Access, 9.
pp. 166695-166708.
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Text (Published Version)
Deep_Multi-Stage_Reference_Evapotranspiration_Forecasting_Model_Multivariate_Empirical_Mode_Decomposition_Integrated_With_the_Boruta-Random_Forest_Algorithm.pdf Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
Evapotranspiration, as a combination of evaporation and transpiration of water vapour, is a primary component of global hydrological cycles. It accounts for significant loss of soil moisture from the earth to the atmosphere. Reliable methods to monitor and forecast evapotranspiration are required for decision-making. Reference evapotranspiration, denoted as ET , is a major parameter that is useful in quantifying soil moisture in a cropping system. This article aims to design a multi-stage deep learning hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Multivariate Empirical Mode Decomposition (MEMD) and Boruta-Random Forest (Boruta) algorithms to forecast ET in the drought-prone regions ( i.e ., Gatton, Fordsdale, Cairns) of Queensland, Australia. Daily data extracted from NASA’s Goddard Online Interactive Visualization and Analysis Infrastructure (GIOVANNI) and Scientific Information for Land Owners (SILO) repositories over 2003–2011 are used to build the proposed multi-stage deep learning hybrid model, i.e ., MEMD-Boruta-LSTM, and the model’s performance is compared against competitive benchmark models such as hybrid MEMD-Boruta-DNN, MEMD-Boruta-DT, and a standalone LSTM, DNN and DT model. The test MEMD-Boruta-LSTM hybrid model attained the lowest Relative Root Mean Square Error (≤17%), Absolute Percentage Bias (≤12.5%)and the highest Kling-Gupta Efficiency (≥0.89%) relative to benchmark models for all study sites. The proposed multi-stage deep hybrid MEMD-Boruta-LSTM model also outperformed all other benchmark models in terms of predictive efficacy, demonstrating its usefulness in the forecasting of the daily ET dataset. This MEMD-Boruta-LSTM hybrid model could therefore be employed in practical environments such as irrigation management systems to estimate evapotranspiration or to forecast ET.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021) |
| Faculty/School / Institute/Centre: | Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -) |
| Date Deposited: | 01 Mar 2022 04:36 |
| Last Modified: | 31 Mar 2022 23:22 |
| Uncontrolled Keywords: | Boruta-random forest algorithm; Deep learning; Long short-term memory network; Multivariate empirical mode decomposition; Reference evapotranspiration forecasting |
| Fields of Research (2008): | 04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040608 Surfacewater Hydrology 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling |
| 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 37 EARTH SCIENCES > 3707 Hydrology > 370704 Surface water hydrology |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences |
| Socio-Economic Objectives (2020): | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280111 Expanding knowledge in the environmental sciences |
| Funding Details: | |
| Identification Number or DOI: | https://doi.org/10.1109/ACCESS.2021.3135362 |
| URI: | http://eprints.usq.edu.au/id/eprint/46829 |
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