Evaluation of the Type and Quantity of Data Appropriate for Model Calibration: Case of Flood Forecasting Models

评估适合模型校准的数据类型和数量：洪水预报模型案例

• 批准号: 8610584
• 负责人: Soroosh Sorooshian
• 金额: $ 12.18万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-08-01 至 1990-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8610584&HistoricalAwards=false
• 关键词: Evaluation; Type; Quantity; Data; Appropriate

This project will investigate the calibration and verification methods used for flood forecasting models, with particular concern to the hydrologic models currently in use for planning, design, and operation of engineering systems to cope with extreme material events. Since the success of any calibration procedures is dependent on the quantity and quality of available and used data, the selection of the "most informative" data sets is of great importance, especially because, to date, existing calibration and verification approaches do not exploit the full potential of the information contained in the available data. A method and guidelines for appropriately evaluating the type and quantity of data used in flood forecasting models will be developed.

本项目将研究校准和验证方法 用于洪水预报模型，特别关注 目前用于规划、设计和运行的水文模型 工程系统来科普极端物质事件。 以来 任何校准程序的成功取决于数量， 现有和使用的数据的质量，选择“最 信息”数据集非常重要，特别是因为， 现有的校准和验证方法不利用 现有数据所含信息的全部潜力。 一种方法和准则，用于适当地评估类型和 将开发洪水预报模型所用的大量数据。