Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data
Article
Article Title | Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data |
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ERA Journal ID | 1949 |
Article Category | Article |
Authors | Jayasinghe, W. J. M. Lakmini Prarthana (Author), Deo, Ravinesh C. (Author), Ghahramani, Afshin (Author), Ghimire, Sujan (Author) and Raj, Nawin (Author) |
Journal Title | Journal of Hydrology |
Journal Citation | 607, pp. 1-19 |
Article Number | 127534 |
Number of Pages | 19 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0022-1694 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jhydrol.2022.127534 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0022169422001093 |
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. |
Keywords | Prediction of pan evaporation; Long short-term memory networks; Neighbourhood component analysis; Deep learning; Hybrid models; Evaporative water loss |
Related Output | |
Is part of | Evaporation and soil moisture prediction with artificial intelligence and deep learning methods |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
370704. Surface water hydrology | |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | School of Mathematics, Physics and Computing |
Centre for Sustainable Agricultural Systems | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/q71vw/development-and-evaluation-of-hybrid-deep-learning-long-short-term-memory-network-model-for-pan-evaporation-estimation-trained-with-satellite-and-ground-based-data
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