Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm. Issue 7 (3rd July 2017)

• Record Type: Journal Article
• Authors: Ouyang, Huei-Tau
• Is Part Of: Engineering optimization. Volume 49:Issue 7(2017)
• Page Start: 1211
• Page End: 1225
• Publication Date: 2017-07-03
• Subjects: Inundation -- typhoon -- ARX -- multi-objective genetic algorithm -- Pareto-optimal set; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1230207
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.