Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model

Fiaz Hussain (), Ray-Shyan Wu () and Jing-Xue Wang ()
Additional contact information
Fiaz Hussain: National Central University
Ray-Shyan Wu: National Central University
Jing-Xue Wang: National Central University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 107, issue 1, No 12, 249-284

Abstract: Abstract Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.

Keywords: Flood forecasting; Early warning; Radar rainfall; SVM model; WASH123D; HEC-HMS (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-04582-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04582-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-04582-3

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().