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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

机译：通过将ARMAX与遗传算法相结合，对跨站点结构的台风淹没预报模型进行多目标优化

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摘要

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.
机译：预报台风期间的淹没水平需要考虑多个目标，包括对整个天气事件中水位变化的预测能力，预报高峰水位时可达到的准确度以及时间。可能会出现最高水位。本文提出了一种利用水位测量网络的监测数据实时预测淹没水位的方法。使用ARMAX，使用输入变量（包括网络中其他测量站的累积降雨和水位数据）输入每个测量站的水位预测模型。对累积降雨和水位数据之间的相关性进行分析，就可以获取适当的降雨累积持续时间和每个计量站的时滞。通过对跨站点水位以及累积降雨进行分析，可以识别与每个测量站相关的关联站点，这些站点在水位方面具有高度相关性，而在累积降雨方面却具有较低的相互信息。来自标识的关联站点的水位数据用作目标站点水位预测模型的第二个输入变量。选择最佳模型时要考虑三个指标：效率系数（CE），峰值水位阶段的误差（ESP）和相对时移（RTS）。采用多目标遗传算法来得出能够在三个目标中表现良好的最优Pareto模型集。在台湾宜兰县新南地区进行了案例研究，为该地区四个水位测量站中的每一个建立了最佳水位预测模型。测试结果表明，最能满足ESP的模型表现出明显的时移，而最能满足CE和RTS的模型在水位变化不太严重时能够准确地预测洪水泛滥。

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