Curceac Stelian, Atkinson Peter M, Milne Alice, Wu Lianhai, Harris Paul
Rothamsted Research, Department of Sustainable Agriculture Sciences, Devon, United Kingdom.
Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, United Kingdom.
Front Artif Intell. 2020 Oct 9;3:565859. doi: 10.3389/frai.2020.565859. eCollection 2020.
Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model's output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.
洪峰事件可能导致洪水泛滥，这会对人类生活和生态系统服务产生负面影响。因此，准确预测此类洪峰至关重要。基于物理的过程模型通常用于模拟水流，但它们往往对峰值事件预测不足（即存在条件偏差），这削弱了它们在洪水预报中的适用性。在本研究中，我们探索了通过将模型输出与以下两者相结合来提高基于过程模型的洪峰模拟准确性的方法：a）半参数条件极值模型和b）极限学习机模型。使用来自英国英格兰西南部一个草地研究站——北怀克农场平台子流域的高时间分辨率水流数据，对所提出的三模型混合方法进行了评估。使用具有多个误差和一致性指标的留一法评估程序，针对其更简单的组成模型对混合模型进行了客观评估。所提出的混合方法能够更好地捕捉水流过程的动态，从而提高洪峰事件的预测准确性。
