利用河流流量观测中气象参数的整合提高洪水预报精度——以渥太华河为例

IF 3.1 Q2 WATER RESOURCES Hydrology Pub Date : 2023-08-10 DOI:10.3390/hydrology10080164
Clara Letessier, Jean-Louis Cardi, A. Dussel, Isa Ebtehaj, H. Bonakdari
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引用次数: 1

摘要

考虑到加拿大安大略省洪水的主要原因是春季洪水,将温度作为机器学习算法的洪水预测模型的输入变量至关重要。这使得人们能够全面了解所涉及的复杂动力学,特别是热浪对融雪的影响,从而实现更准确的洪水预测。本文提出了一种新的机器学习方法,称为数据处理组方法(ASGMDH)的自适应结构,用于预测每日河流流量,将前一天的测量流量作为总结流域特征的历史记录,以及空气温度和降水的实时数据。为了提出一个全面的机器学习模型,研究了四种不同输入组合的不同场景。最简单的三个参数(最高温度、降水量、历史日河流量)模型精度较高,训练时的R2值为0.985,测试时的R2值为0.992,显示了模型的可靠性和实际应用潜力。所建立的ASGMDH模型在研究区域具有较高的精度,大量样本的相对误差小于15%。最终的基于asgmdh的模型只有一个二阶多项式(AICc = 19,648.71),而经典的基于gmdh的模型有7个二阶多项式(AICc = 19,701.56)。敏感性分析表明,最高气温对日流量预测有显著影响。
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Enhancing Flood Prediction Accuracy through Integration of Meteorological Parameters in River Flow Observations: A Case Study Ottawa River
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehensive understanding of the intricate dynamics involved, particularly the impact of heatwaves on snowmelt, allowing for more accurate flood prediction. This paper presents a novel machine learning approach called the Adaptive Structure of the Group Method of Data Handling (ASGMDH) for predicting daily river flow rates, incorporating measured discharge from the previous day as a historical record summarizing watershed characteristics, along with real-time data on air temperature and precipitation. To propose a comprehensive machine learning model, four different scenarios with various input combinations were examined. The simplest model with three parameters (maximum temperature, precipitation, historical daily river flow discharge) achieves high accuracy, with an R2 value of 0.985 during training and 0.992 during testing, demonstrating its reliability and potential for practical application. The developed ASGMDH model demonstrates high accuracy for the study area, with a significant number of samples having a relative error of less than 15%. The final ASGMDH-based model has only a second-order polynomial (AICc = 19,648.71), while it is seven for the classical GMDH-based model (AICc = 19,701.56). The sensitivity analysis reveals that maximum temperature significantly impacts the prediction of daily river flow discharge.
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来源期刊
Hydrology
Hydrology Earth and Planetary Sciences-Earth-Surface Processes
CiteScore
4.90
自引率
21.90%
发文量
192
审稿时长
6 weeks
期刊介绍: Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences, including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology, hydrogeology and hydrogeophysics. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, ecohydrology, geomorphology, soil science, instrumentation and remote sensing, data and information sciences, civil and environmental engineering are within scope. Social science perspectives on hydrological problems such as resource and ecological economics, sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site. Studies focused on urban hydrological issues are included.
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