Daily flood forecasts with intelligent data analytic models: multivariate empirical mode decomposition-based modeling methods

Prasad, Ramendra and Charan, Dhrishna and Joseph, Lionel and Nguyen-Huy, Thong ORCID: https://orcid.org/0000-0002-2201-6666 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Singh, Sanjay (2021) Daily flood forecasts with intelligent data analytic models: multivariate empirical mode decomposition-based modeling methods. In: Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore, pp. 359-381. ISBN 978-981-15-5771-2


Flood causes massive damages to infrastructure, agriculture, livelihood and leads to loss of life. This chapter designs M5 tree-based machine learning model integrated with advanced multivariate empirical mode decomposition (i.e., MEMD-M5 Tree) for daily flood index (FI) forecasting for Lockyer Valley in southeast Queensland, Australia, using data from January 01, 1950, to December 31, 2012. The MEMD-M5 tree is evaluated against MEMD-RF, standalone M5 tree, and RF models via statistical metrics, diagnostic plots with error distributions between simulated and observed daily flood index. The results indicate that MEMD-M5 tree outperforms the comparative models by attaining maximum values of r = 0.990, WI = 0.992, ENS = 0.979, and L = 0.920. The MEMD-M5 tree outperforms other models by registering the least value of RMSE and MAE and can precisely emulate 97.94% of daily FI value. Graphical diagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a greater resemblance to that of the observed data supporting the outcomes of statistical evaluation. Such advancements in flood prediction models, attained through data intelligent analytical methods, are very vital and effective in ensuring better mitigation and civil protection in emergency providing an early warning system, disaster risk reduction, disaster policy suggestions, and reduction of the property damage.

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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published chapter in accordance with the copyright policy of the publisher.
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 Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 02 Sep 2020 02:47
Last Modified: 03 Dec 2021 03:03
Uncontrolled Keywords: MEMD; M5 tree; flood index forecasting; disaster risk reduction; machine learning
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1007/978-981-15-5772-9_17
URI: http://eprints.usq.edu.au/id/eprint/39218

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