Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data

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 (2022) Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data. Journal of Hydrology, 607:127534. pp. 1-19. ISSN 0022-1694


Abstract

Evaporation, as a core process within the global hydrological cycle, requires reliable methods to monitor its variation, for decision-making in agriculture, irrigation systems and dam operations, also in other areas of hydrology and water resource management. Accurate monitoring of pan evaporation (Ep) is one the most popular approaches to understand the evaporative process. This work aims to construct a hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Neighbourhood Component Analysis for feature selection to predict in drought-prone regions in Queensland, Australia (Amberley, Gatton, Oakey, & Townsville). Utilizing the daily-scale dataset [31 August 2002 to 22 September 2020], the performance of the proposed deep learning (DL) hybrid model, denoted as NCA-LSTM, is compared with competitive benchmark models, i.e., standalone LSTM, other types of DL, single hidden layer neuronal architecture and decision tree-based method. The testing results reveal the lowest Relative Root Mean Square Error , Absolute Percentage Bias and the highest Kling-Gupta Efficiency attained by the NCA-LSTM hybrid model (relative to benchmark models) tested for Amberley, Gatton, and Oakey sites. In respect to the predictive efficiency, the proposed NCA-LSTM hybrid model, improved with feature selection, outperforms all benchmark models, indicating its future utility in the prediction of daily Ep. In practical sense, the predictive model developed for Ep estimation provides an accurate estimation of evaporative water loss in hydrological cycle and therefore, can be implemented in areas of irrigation management, planning of irrigation-based agriculture, and mitigation of financial losses to agricultural and related sectors where, regular monitoring and forecasting of water resources are a vital part of sustainable livelihood and business.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 22 Mar 2022 00:05
Last Modified: 22 Mar 2022 00:05
Uncontrolled Keywords: Prediction of pan evaporation; Long short-term memory networks; Neighbourhood component analysis; Deep learning; Hybrid models; Evaporative water loss
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
37 EARTH SCIENCES > 3707 Hydrology > 370704 Surface water hydrology
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280111 Expanding knowledge in the environmental sciences
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.1016/j.jhydrol.2022.127534
URI: http://eprints.usq.edu.au/id/eprint/47168

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