Jucazinho 大坝的水流预测:机器学习技术比较分析

Erickson Johny Galindo da Silva, Artur Paiva Coutinho, Jean Firmino Cardoso, Saulo de Tarso Marques Bezerra
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引用次数: 0

摘要

从 Saad el-Kafara 大坝到 20 世纪 50 年代的全球扩张,几个世纪的大坝建设史凸显了这些建筑在水资源管理中的重要性。建于 1998 年的 Jucazinho 大坝是为了应对巴西伯南布哥州阿格里斯特地区的缺水问题而兴建的。2016 年,大坝的蓄水能力不足 1%,在当地水务公司的干预下,大坝于 2020 年恢复了蓄水能力。在这种情况下,大坝进水流量预测模型的可靠性对管理者来说至关重要。本研究提出了基于人工智能的水文模型,旨在生成流量序列,并评估了这些模型对 Jucazinho 大坝运行的适应性。数据在 0 和 1 之间进行了归一化处理,以避免高值变量占主导地位。该模型基于机器学习,采用了 Python Sklearn 库提供的支持向量回归 (SVM)、随机森林 (RF) 和人工神经网络 (ANN)。监测站的选择是通过巴西国家水和卫生局(ANA)的 HIDROWEB 门户网站进行的,我们使用斯皮尔曼相关性来确定降水和流量之间的关系。对模型性能的评估包括图形分析和统计标准,如纳什-萨特克利夫模型效率系数(NSE)、偏差百分比(PBIAS)、判定系数(R2)和根平均标准偏差率(RSR)。测试数据的统计系数结果表明,长期预测(提前 8 天、16 天和 32 天)的表现并不令人满意,随着预测范围的扩大,拟合质量呈下降趋势。SVM 模型在 NSE、PBIAS、R2 和 RSR 方面获得了最佳指数,表现突出。SVM 模型的图形结果显示,随着预测范围的增加,流量值被低估,这是因为 SVM 对时间序列中的复杂模式非常敏感。另一方面,随着预报天数的增加,RF 和 ANN 模型高估了流量值,这主要是由于过度拟合造成的。总之,本研究强调了人工智能在流量预测中的相关性,以促进大坝的有效管理,尤其是在缺水和数据稀缺的情况下。正确选择模型和确保可靠的输入数据是获得准确预测的关键,有助于水安全和 Jucazinho 等水坝的有效运行。
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Jucazinho Dam Streamflow Prediction: A Comparative Analysis of Machine Learning Techniques
The centuries-old history of dam construction, from the Saad el-Kafara Dam to global expansion in the 1950s, highlights the importance of these structures in water resource management. The Jucazinho Dam, built in 1998, emerged as a response to the scarcity of water in the Agreste region of Pernambuco, Brazil. After having less than 1% of its water storage capacity in 2016, the dam recovered in 2020 after interventions by the local water utility. In this context, the reliability of influent flow prediction models for dams becomes crucial for managers. This study proposed hydrological models based on artificial intelligence that aim to generate flow series, and we evaluated the adaptability of these models for the operation of the Jucazinho Dam. Data normalization between 0 and 1 was applied to avoid the predominance of variables with high values. The model was based on machine learning and employed support vector regression (SVM), random forest (RF) and artificial neural networks (ANNs), as provided by the Python Sklearn library. The selection of the monitoring stations took place via the Brazilian National Water and Sanitation Agency’s (ANA) HIDROWEB portal, and we used Spearman’s correlation to identify the relationship between precipitation and flow. The evaluation of the performance of the model involved graphical analyses and statistical criteria such as the Nash–Sutcliffe model efficiency coefficient (NSE), the percentage of bias (PBIAS), the coefficient of determination (R2) and the root mean standard deviation ratio (RSR). The results of the statistical coefficients for the test data indicated unsatisfactory performance for long-term predictions (8, 16 and 32 days ahead), revealing a downward trend in the quality of the fit with an increase in the forecast horizon. The SVM model stood out by obtaining the best indices of NSE, PBIAS, R2 and RSR. The graphical results of the SVM models showed underestimation of the flow values with an increase in the forecast horizon due to the sensitivity of the SVM to complex patterns in the time series. On the other hand, the RF and ANN models showed hyperestimation of the flow values as the number of forecast days increased, which was mainly attributed to overfitting. In summary, this study highlights the relevance of artificial intelligence in flow prediction for the efficient management of dams, especially in water scarcity and data-scarce scenarios. A proper choice of models and the ensuring of reliable input data are crucial for obtaining accurate forecasts and can contribute to water security and the effective operation of dams such as Jucazinho.
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