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.

翻译：极端事件的风险评估需要准确估计超出历史观测范围的高分位数。当风险取决于观测预测变量的值时，回归技术被用于在预测变量空间中进行插值。我们提出了EQRN模型，该模型将神经网络与极值理论工具相结合，形成一种能够在存在复杂预测变量依赖关系时进行外推的方法。神经网络能够自然地整合数据中的附加结构。我们开发了EQRN的循环版本，该版本能够捕捉时间序列中复杂的序列依赖性。我们将此方法应用于瑞士阿勒河流域的洪水风险预测。该方法利用来自空间和时间上多个协变量的信息，提供提前一天的回归水平预测和超越概率预测。该输出结果补充了传统极值分析中的静态回归水平，并且其预测能够适应气候变化中经历的分布偏移。我们的模型可以帮助当局通过早期预警系统更有效地管理洪水，并最大限度地减少其灾难性影响。