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Flood forecasting in large rivers with data-driven models

Nguyen, Phouc-Tien, Chua, Lloyd Hock-Chye and Sin, Lam Hung 2014, Flood forecasting in large rivers with data-driven models, Natural hazards, vol. 71, no. 1, pp. 767-784, doi: 10.1007/s11069-013-0920-7.

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Title Flood forecasting in large rivers with data-driven models
Author(s) Nguyen, Phouc-Tien
Chua, Lloyd Hock-ChyeORCID iD for Chua, Lloyd Hock-Chye orcid.org/0000-0003-2523-3735
Sin, Lam Hung
Journal name Natural hazards
Volume number 71
Issue number 1
Start page 767
End page 784
Total pages 18
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014
ISSN 0921-030X
Keyword(s) natural hazards
hydrogeology
geophysics
geotechnical engineering & applied earth sciences
civil engineering
environmental management
Summary Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.
Language eng
DOI 10.1007/s11069-013-0920-7
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30063879

Document type: Journal Article
Collection: School of Engineering
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Created: Mon, 02 Jun 2014, 10:20:53 EST

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