论文标题
用于极端分位回归的神经网络，并应用于预测洪水风险
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
论文作者
论文摘要
极端事件的风险评估需要对超出历史观察范围的高分位数进行准确估算。当风险取决于观察到的预测因子的值时，回归技术用于在预测器空间中插值。我们提出了将神经网络和极值理论的工具结合到能够在存在复杂预测依赖性的情况下推断出外推的方法的EQRN模型。神经网络自然可以在数据中纳入其他结构。我们开发了EQRN的经常性版本，该版本能够捕获时间序列中的复杂顺序依赖性。我们将这种方法应用于瑞士AARE集水区的洪水风险。它利用从时空和时间中的多个协变量中利用信息，以提供回报水平和超出概率的一日预测。该输出从传统的极值分析中补充了静态返回水平，并且预测能够适应不断变化的气候中经历的分配变化。我们的模型可以帮助当局通过预警系统更有效地管理洪水，并最大程度地减少其灾难性影响。
Risk assessment for extreme events requires accurate estimation of high quantiles that go beyond the range of historical observations. When the risk depends on the values of observed predictors, regression techniques are used to interpolate in the predictor space. We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence. Neural networks can naturally incorporate additional structure in the data. We develop a recurrent version of EQRN that is able to capture complex sequential dependence in time series. We apply this method to forecast flood risk in the Swiss Aare catchment. It exploits information from multiple covariates in space and time to provide one-day-ahead predictions of return levels and exceedance probabilities. This output complements the static return level from a traditional extreme value analysis, and the predictions are able to adapt to distributional shifts as experienced in a changing climate. Our model can help authorities to manage flooding more effectively and to minimize their disastrous impacts through early warning systems.
