A machine learning-based monthly streamflow forecast model for Alberta

艾伯塔省基于机器学习的月度流量预测模型

• 批准号: 572371-2022
• 负责人: Davies, EvanEGR
• 金额: $ 2.19万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748909
• 关键词: machine; learning; based; monthly; streamflow

This project will develop a machine-learning (ML) based monthly streamflow forecast tool for two hydrological stations in Alberta: the Oldman River at Lethbridge and the Red Deer River at Dickson Dam. The tool will provide monthly forecasts for March-September (7 predictions per season), with month-by-month updating through incorporating data from the previous month to improve subsequent monthly forecasts. Machine-learning algorithms offer great promise in the field of hydrology, and are increasingly widely-applied for streamflow forecasting, estimation of flow duration curves, rainfall-runoff modelling, and urban water demand forecasting. This project will test the performance of several ML algorithms, including artificial neural networks (ANN), support vector machines (SVM), extreme learning machines (ELM), and radial basis function networks (RBF), for monthly streamflow forecasts. Such forecasts are critical for optimal operation of reservoirs, spring seeding, and water use efficiency improvements, and help to provide early flooding and drought warning. Further, climate change-driven intensification of the hydrological cycle, and its corresponding increase in frequency and magnitude of flood and drought events, makes such forecasting tools increasingly important and valuable. Once the performance of the models is evaluated and deemed to be acceptable, the streamflow forecasts will be postable online by the partner organization, Alberta Environment and Parks, for use by Canadian irrigation districts, municipalities, industries, businesses, and the public downstream of the stations in Alberta. Both the dynamic month-by-month updating of the forecasts and their intended direct application to operations are novel and important characteristics of the research.

该项目将为艾伯塔省的两个水文站开发基于机器学习 (ML) 的月度流量预测工具：莱斯布里奇的奥德曼河和迪克森大坝的红鹿河。该工具将提供 3 月至 9 月的月度预测（每个季节 7 个预测），并通过合并上个月的数据进行逐月更新，以改进后续的月度预测。 机器学习算法在水文学领域提供了巨大的前景，并且越来越广泛地应用于径流预测、流量持续时间曲线估计、降雨径流建模和城市需水预测。 该项目将测试多种机器学习算法的性能，包括人工神经网络 (ANN)、支持向量机 (SVM)、极限学习机 (ELM) 和径向基函数网络 (RBF)，用于每月的水流预测。 此类预报对于水库优化运行、春季播种和提高用水效率至关重要，并有助于提供早期洪水和干旱预警。 此外，气候变化驱动的水文循环加剧，以及相应的洪水和干旱事件频率和强度的增加，使得此类预测工具变得越来越重要和有价值。 一旦模型的性能经过评估并被认为可以接受，水流预测将由合作组织阿尔伯塔环境和公园在线发布，供加拿大灌溉区、市政当局、工业、企业和下游公众使用。艾伯塔省的车站。 预测的逐月动态更新及其预期直接应用于运营都是该研究的新颖且重要的特征。