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 multi-objective 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 water-level 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.
Highlights
Typhoons are common weather events in subtropical regions of the Pacific, between July and October
All of the typhoon events were validated, and the performance of this model structure was represented by the averaged coefficient of efficiency (CE), ESP, and relative time shift (RTS) over all the validation cases. This procedure was integrated with a multiobjective genetic algorithm (MOGA) introduced in the following to search for the optimal models that perform well in all the three indices for each gauging station
We listed three models for each gauging station and named them according to their location
Summary
Typhoons are common weather events in subtropical regions of the Pacific, between July and October. Toth et al (2000) compared the advantages and limitations of the auto-regressive moving average, artificial neural network (ANN), and non-parametric nearest-neighbor method in rainfall–runoff forecasting They concluded that time series analysis is far more accurate than simple rainfall predictions of a heuristic nature. Nayak et al (2005) employed fuzzy computation in the development of a realtime flood forecasting model They concluded that the recursive use of a one-step-ahead forecast model to predict flow using longer lead times produces results better than those achieved using independent fuzzy models for the forecasting of flow under various lead times. Chen et al (2006) constructed a flood forecast model using an adaptive neuro-fuzzy inference system (ANFIS) Their results demonstrated that ANFIS is superior to back-propagation neural network. We sought to develop a method for the forecasting of inundation levels, based on data from a water-level gauging network during typhoons.
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