Multi-objective optimization of typhoon inundation forecast models with
cross-site structures for a water-level gauging network by integrating ARMAX
with a genetic algorithm

Ouyang, Huei-Tau

doi:https://doi.org/10.5194/nhess-16-1897-2016

Articles | Volume 16, issue 8

https://doi.org/10.5194/nhess-16-1897-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/nhess-16-1897-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 8

Research article

|

16 Aug 2016

Research article |

| 16 Aug 2016

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

Huei-Tau Ouyang

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Final revised paper (published on 16 Aug 2016)
Preprint (discussion started on 23 Feb 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'none', Anonymous Referee #1, 15 Apr 2016
- SC1: 'Comments for "Multi-Objective optimization of typhoon inundation forecast models for water-level gauging network by integration of ARMAX with a Genetic Algorithm"', SHANG-SHU SHIH, 31 May 2016
  - AC1: 'Reply to the comment', Huei-Tau Ouyang, 31 May 2016
SC2: 'comments for Multi-Objective optimization of typhoon inundation forecast models for water-level gauging network by integration of ARMAX with a Genetic Algorithm', J.-S. Lai, 31 May 2016
- AC2: 'Reply to the Referee's comment', Huei-Tau Ouyang, 06 Jun 2016
SC3: ''Comments for "Multi-Objective optimization of typhoon inundation forecast models for water-level gauging network by integration of ARMAX with a Genetic Algorithm"'', GWO-WEN HWANG, 15 Jun 2016
- AC3: 'Reply to the comment', Huei-Tau Ouyang, 20 Jun 2016
RC2: 'Reviewer Comments', Anonymous Referee #2, 05 Jul 2016
- AC4: 'Reply to the comment', Huei-Tau Ouyang, 07 Jul 2016

Short summary

Typhoon inundation forecast model for a gauging network is proposed. Model inputs are identified by cross-correlation and mutual information analysis. Optimal ARMAX model structures are searched for considering three objective functions, including the forecasting capacity in water level throughout the event, the accuracy in forecasting peak water levels and the time at which peak water levels are likely to occur. Characteristics of the resultant models subject to various objectives are examined.