每日流估计的深度和机器学习:关注LSTM, RFR和XGBoost

IF 1.6 Q3 WATER RESOURCES Water Practice and Technology Pub Date : 2023-09-22 DOI:10.2166/wpt.2023.144
Özlem Terzi, Ecir Uğur Küçüksille, Tahsin Baykal, Dilek Taylan
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引用次数: 0

摘要

摘要流量值的估算精度对水资源的长期规划和采取措施应对旱涝灾害具有重要意义。河流流域内形成的水流具有复杂的物理结构,其变化取决于流域特征(如地形和植被)、气象因素(如降水、蒸发和入渗)以及人类活动。近年来,深度学习和机器学习技术因其强大的学习能力和对这些复杂非线性过程的准确可靠的建模而备受关注。本文采用长短期记忆(LSTM)、随机森林回归(RFR)和极端梯度增强(XGBoost)方法估算了土耳其Göksu河的日流量值。Hyperparameter优化实现深度和机器学习算法。采用1990-2010年的日流量值,并在建模中尝试了各种输入参数。检查性能(R2、RMSE和梅)的模型,XGBoost模型有五个输入参数提供了比其他模型更合适的结果。测试集XGBoost模型的R2值为0.871。此外,还成功地使用了深度和机器学习算法进行流量估计。
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Deep and machine learning for daily streamflow estimation: a focus on LSTM, RFR and XGBoost
Abstract Estimation accuracy of streamflow values is of great importance in terms of long-term planning of water resources and taking measures against disasters such as drought and flood. The flow formed in a river basin has a complex physical structure that changes depending on the characteristics of the basin (such as topography and vegetation), meteorological factors (such as precipitation, evaporation and infiltration) and human activities. In recent years, deep and machine learning techniques have attracted attention thanks to their powerful learning capabilities and accurate and reliable modeling of these complex and nonlinear processes. In this paper, long short-term memory (LSTM), random forest regression (RFR) and extreme gradient boosting (XGBoost) approaches were applied to estimate daily streamflow values of Göksu River, Turkey. Hyperparameter optimization was realized for deep and machine learning algorithms. The daily flow values between the years 1990–2010 were used and various input parameters were tried in the modeling. Examining the performance (R2, RMSE and MAE) of the models, the XGBoost model having five input parameters provided more appropriate results than other models. The R2 value of the XGBoost model was obtained as 0.871 for the testing set. Also, it is shown that deep and machine learning algorithms are used successfully for streamflow estimation.
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来源期刊
CiteScore
2.30
自引率
6.20%
发文量
136
审稿时长
14 weeks
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