New double decomposition deep learning methods for river water level forecasting

Ahmed, A. A. Masrur and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Feng, Qi and Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644 and Yin, Zhenliang and Yang, Linshan (2022) New double decomposition deep learning methods for river water level forecasting. Science of the Total Environment, 831:154722. pp. 1-21. ISSN 0048-9697


Abstract

Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
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: 26 May 2022 02:07
Last Modified: 26 May 2022 02:07
Uncontrolled Keywords: River water level; Satellite data; Climate indices; Deep hybrid learning; Feature extraction; Feature decomposition; Murray River
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 > 280115 Expanding knowledge in the information and computing sciences
Funding Details:
Identification Number or DOI: https://doi.org/10.1016/j.scitotenv.2022.154722
URI: http://eprints.usq.edu.au/id/eprint/48573

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