Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time‑Varying Filtered Empirical Mode Decomposition Approach

Jamei, Mehdi and Ali, Mumtaz ORCID: https://orcid.org/0000-0002-6975-5159 and Malik, Anurag and Prasad, Ramendra and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 and Yaseen, Zaher Mundher (2022) Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time‑Varying Filtered Empirical Mode Decomposition Approach. Water Resources Management. pp. 1-40. ISSN 0920-4741


Abstract

Accurate water level forecasting is important to understand and provide an early warning of food risk and discharge. It is also crucial for many plants and animal species that needs specific ranges of water level. This research focused on long term multi-step ahead forecasting of daily food water level in duration of (2005–2021) at two stations (i.e., Baryulgil and Lilydale) of the Clarence River, in Australia, introducing a novel hybrid framework coupling time varying flter-based empirical mode decomposition (TVF-EMD), classification and regression trees (CART) feature selection, and four advanced machine learning (ML) models. The implemented ML approaches are including Long-Short Term Memory (LSTM), cascaded forward neural network (CFNN), gradient boosting decision tree (GBDT), and multivariate adaptive regression spline (MARS). Here, original time series of WL in each station was decomposed into the optimal intrinsic mode functions (IMFs) using the TVF-EMD technique and the significant lagged-time components for two desired horizons (t+1 and t+7 time ahead) in each station was extracted by using the CART-feature selection method. Then, the IMFs and corresponded residual obtained from the pre-processing procedure were separately implemented to feed the ML models and produce the CART-TVF-EMD-LSTM, CART-TVF-EMD-CFNN, CART-TVF-EMD-MARS, and CART-TVF-EMD-GBDT by assembling all the individual sub-sequences outcomes. Several goodness-of-ft metrics such as correlation coefficient (R), Mean absolute percentage error (MAPE), and Kling-Gupta efficiency (KGE) and the infographic tools and diagnostic analysis were employed to evaluate the robustness of the provided techniques. The outcomes of developed expert systems ascertained that CART-TVF-EMD-CFNN for one- and seven-day horizons in both stations outperformed the CART-TVF-EMD-MARS, CART-TVF-EMD-LSTM, CART-TVF-EMD-GBDT, and all the standalone counterpart models (i.e., CFNN, MARS, LSTM, and GBDT) respectively. As one of the most important achievements of this research, the LSTM did not lead to superior and promising results in the long-term highly nonstationary time series.


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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 - USQ College (8 Jun 2020 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 04 Aug 2022 03:20
Last Modified: 28 Sep 2022 05:24
Uncontrolled Keywords: Flood warning; TVF-EMD; CFNN; Feature selection; LSTM; MARS
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
Socio-Economic Objectives (2020): 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1999 Other environmental policy, climate change and natural hazards > 199999 Other environmental policy, climate change and natural hazards not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/s11269-022-03270-6
URI: http://eprints.usq.edu.au/id/eprint/50654

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