首页> 外文期刊>Journal of the American Water Resources Association >DEVELOPMENT AND OPERATIONAL TESTING OF A SUPER-ENSEMBLE ARTIFICIAL INTELLIGENCE FLOOD-FORECAST MODEL FOR A PACIFIC NORTHWEST RIVER
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DEVELOPMENT AND OPERATIONAL TESTING OF A SUPER-ENSEMBLE ARTIFICIAL INTELLIGENCE FLOOD-FORECAST MODEL FOR A PACIFIC NORTHWEST RIVER

机译:西北太平洋超级包容性人工智能洪水预报模型的开发与运行测试

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Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall- and snowmelt-driven contributions to flood events. Few operational flood-forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super-ensemble forecast system for the Englishman River, a flood-prone stream on Vancouver Island. This three-day-ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network-based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high-flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013-2014 storm season suggest that the prediction system is operationally useful and robust.
机译:加拿大不列颠哥伦比亚省的沿海流域经历了由降雨和融雪驱动的洪水事件的复杂混合。该地区很少有可操作的洪水预报模型。在这里,我们以新颖的方式集成了许多已验证的技术,从而为英吉曼河(温哥华岛上易发洪水的溪流)制作了超集合预报系统。这个为期三天的建模系统利用了来自北美合奏预报系统的多达42个数值天气预报模型输出,并结合了六个基于人工神经网络的流模型,这些流模型分别代表了略有不同的系统概念,所有这些模型都专门针对历史进行了训练高流量数据。因此,该系统将相对较短的模型开发时间和成本与生成的完全概率预测结合在一起,从而反映了大气和陆地水文动力学模拟中的不确定性。不列颠哥伦比亚省洪水预报机构在2013-2014年暴风雨季节期间进行的运行测试结果表明,该预测系统在操作上非常有用且可靠。

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