Daily flood forecasts with intelligent data analytic models: multivariate empirical mode decomposition-based modeling methods
Edited book (chapter)
Chapter Title | Daily flood forecasts with intelligent data analytic models: multivariate empirical mode decomposition-based modeling methods |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 3337 |
Book Title | Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation |
Authors | Prasad, Ramendra (Author), Charan, Dhrishna (Author), Joseph, Lionel (Author), Nguyen-Huy, Thong (Author), Deo, Ravinesh C. (Author) and Singh, Sanjay (Author) |
Editors | Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher |
Page Range | 359-381 |
Series | Springer Transactions in Civil and Environmental Engineering |
Chapter Number | 17 |
Number of Pages | 23 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Singapore |
ISBN | 9789811557712 |
9789811557729 | |
ISSN | 2363-7633 |
2363-7641 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-15-5772-9_17 |
Web Address (URL) | https://link.springer.com/chapter/10.1007/978-981-15-5772-9_17 |
Abstract | 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. |
Keywords | MEMD; M5 tree; flood index forecasting; disaster risk reduction; machine learning |
ANZSRC Field of Research 2020 | 410402. Environmental assessment and monitoring |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Fiji, Fiji |
Centre for Applied Climate Sciences | |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5wqz/daily-flood-forecasts-with-intelligent-data-analytic-models-multivariate-empirical-mode-decomposition-based-modeling-methods
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