An ensemble learning framework for long-term flood forecasting

考虑到洪水风险，预报河流洪水对水资源管理和风险防范具有重要意义。即使长期洪水预报是一项艰巨的任务，但对数洪水预报是非常有用的，例如，让市政当局有足够的时间准备和行动。建立长期预报框架的努力通常面临来自天气动态的挑战。最近，机器学习技术已被公认并被广泛用于可持续基础设施中的复杂问题的建模，特别是在预测极端事件方面。特别是，机器学习技术已成功地应用于洪水预报，并提供了改进的预报技术和相对更准确的结果。最近，合奏学习引起了相当大的兴趣。与单一模型相比，集成学习提供了更稳定的预测性能，推动了集成学习的不确定性行为的减少。该研究项目的主要目标是开发基于集成的机器学习(EML)模型，用于不同信息标准和有限极端事件历史条件下的河流流量长期预报。新的、高度准确和可靠的长期预测模型将为美国以及加拿大各地的客户提供非常有用的模型，这些模型将显著改善长期预测，从而有助于有效和可持续地规划和管理极端事件应对策略。

Given flood risks, forecasting river flood is important for water resource management and risk preven-tion.Even though long-term flood forecasting is a difficult task, log-term flood forecasting is very useful forinstance to let municipalities to have enough time for preparation and action. The efforts in ultimately creatinga long-term forecasting framework are usually faced with the challenges stemming from weather dynamics.Machine learning techniques have recently been recognized and widely adopted for modeling complexproblems in sustainable infrastructures, especially in forecasting extreme events. In particular, machinelearning techniques have been successfully applied for flood forecasting and provided improved forecastingtechniques and relatively more accurate results. More recently, Ensemble learning has re-ceived a significantamount of interest. Ensemble learning provides a more stable prediction performance compared to singlemodel, driving the diminishing uncertainty behaviour of ensemble learning. The main goal of this researchproject is to develop ensemble based machine learning (EML) models for the long-term forecasting of riverflow under different information criterion and limited history of extreme events. The new, highly accurate andreliable long-term forecasting models will provide US, and in turn their clients across Canada, with very usefulmodels that will allow significantly improved long-term forecast-ing and will consequently help in effectivelyand sustainably plan and manage extreme events response strategies.