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

Abstract

Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multiobjective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four waterlevel gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

Optimal Combinations of Non-Sequential Regressors for ARX-Based Typhoon Inundation Forecast Models Considering Multiple Objectives

“…However, it has been reported by Ouyang [19] that the variation in water level is slower than the variation in rainfall, and often a certain amount of rainfall accumulation is required for the water to build up. To search for the most correlated cumulative rainfall to the water level at the study site, the procedure of Ouyang [19] was adopted here. The moving cumulative rainfall data of the typhoon events with accumulation duration ranging from 1 h to 30 h were first constructed.…”

Real-time forecasting of typhoon inundation extent in a partially-gauged area through the integration of ARX-based models and a geographic information system

“…The Xinnan area studied by Ouyang (2016) was selected as the study area. The area covers 6.5 km2 and the elevation of terrain in the area averages approximately 2 meters only.…”

“…The study extended Ouyang (2016) by the following improvements: (1) A geographic information system (HEC-GeoHMS) is incorporated in the 30 proposed methodology to analyse the digital elevation model (DEM) of the study area and to divide the area into sub-areas; (2) The methodology proposed in the present study is capable of not only forecasting future inundation-levels but also the extent of inundation in the area by incorporating DEM and the flatwater assumption; (3) The rainfall water-level models in the present study are improved by taking in the simulated inundation-level data from the inundation potential database (IPDB) as inputs, and thus are 35 applicable for locations where no observation data is available; and (4) In the present study, not only the gauged locations but also the ungauged representative points (outlets of the sub-areas) are modelled. This paper is arranged as follows.…”

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

“…Recently, based on this progress in CI, many nature inspired approaches have been proposed to solve test selection optimization problem, such as the greedy strategy [53], the genetic algorithm [54,55], the evolutionary algorithm [56,57], and so on [58]. The PSO has some advantages in comparison with some other methods; for example, it requires less parameters and has a fast convergence rate.…”