基于机器学习的冰冻圈典型流域水文过程模拟研究

doi:10.13745/j.esf.sf.2023.2.52

地学前缘 ›› 2023, Vol. 30 ›› Issue (4): 451-469.DOI: 10.13745/j.esf.sf.2023.2.52

基于机器学习的冰冻圈典型流域水文过程模拟研究

宋轩宇¹^,³(), 许民¹^,²^,^*(), 康世昌¹^,², 孙立平⁴

1.中国科学院西北生态环境资源研究院冰冻圈科学国家重点实验室, 甘肃兰州 730000
2.中国科学院大学, 北京 100049
3.兰州交通大学数理学院, 甘肃兰州 730070
4.东北大学理学院, 辽宁沈阳 110819

收稿日期:2023-01-28 修回日期:2023-02-20 出版日期:2023-07-25 发布日期:2023-07-07
通讯作者: *许　民(1984—),男,副研究员,主要研究方向为冰冻圈水文与水资源研究。E⁃mail: xumin@lzb.ac.cn
作者简介:宋轩宇(1998-),男,硕士研究生,主要方向为气候变化与冰冻圈水文过程模拟研究。E-mail: songxuanyu2022@163.com
基金资助:
国家自然科学基金项目(41971094);国家自然科学基金项目(41871055);中国科学院青年创新促进会人才项目(2019414);中国科学院-澳大利亚联邦科学和工业研究组织(CAS-CSIRO)国际合作项目(131B62KYSB20190042)

Modeling of hydrological processes in cryospheric watersheds based on machine learning

SONG Xuanyu¹^,³(), XU Min¹^,²^,^*(), KANG Shichang¹^,², SUN Liping⁴

1. State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou 730000, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Mathematics, Lanzhou Jiaotong University, Lanzhou 730070, China
4. School of Science, Northeastern University, Shenyang 110819, China

Received:2023-01-28 Revised:2023-02-20 Online:2023-07-25 Published:2023-07-07

摘要/Abstract

摘要：

机器学习模型由于其优越的模拟预测性能而被广泛地应用于水文学研究,但其在高海拔地区的冰冻圈流域水文过程模拟研究方面尚有待深入。本研究基于Back Propagation神经网络(BP)、广义回归神经网络(GRNN)、径向基函数神经网络(RBF)、支持向量回归(SVR)、遗传优化BP神经网络(GA-BP)和双层长短期记忆神经网络模型(LSTM),对两个典型冰冻圈流域,即叶尔羌河流域和疏勒河流域的水文过程开展模拟研究,结合精度评价指标(NSE、RMSE和R)以及水文过程频率曲线对模型模拟效果进行综合分析。结果表明,双层LSTM模拟能力在叶尔羌河流域远优于其他模型,而疏勒河流域LSTM模拟效果与其他模型模拟结果相近,双层LSTM更适用于冰冻圈流域水文过程模拟。通过损失函数对模型参数化方案进行评价发现,LSTM模型在研究区模拟效果主要受优化器影响,叶尔羌流域学习衰减速率和初始学习率影响次之,而疏勒河流域初始学习率影响次之。对整个研究时段的径流突变检验分析结果表明,模型输入数据中降水和极端降水总量对研究区水文过程变化影响较大,气温次之。

关键词: 气候变化, 机器学习, 冰冻圈流域, 水文过程模拟, 参数化

Abstract:

Machine learning models are widely used in hydrological research for their high predictive accuracy, however, their application in high-altitude cryospheric watersheds is seldom mentioned. In this study, machine learning models for two typical cryospheres, Yarkant and Shule river basins, were developed using BP neural network (BP), GRNN neural network (GRNN), RBF neural network (RBF), support vector regression (SVR), genetic optimization BP neural network (GA-BP) and double-layer long-term and short-term memory neural network (LSTM) algorithms, and model performance was evaluated using evaluation indexes NSE, RMSE and R and runoff frequency curves. The double-layer LSTM model performed much better than and similar to other models for the Yarkant and Shule River Basins, respectively; and overall the double-layer LSTM algorithm was more suitable for modeling hydrological processes in cryosphere basins. The loss function was used to evaluate the model parameterization scheme. It was found that the performance of the LSTM models was mainly affected by the optimizer, followed by the learning attenuation rate and initial learning rate for the Yarkant River Basin, and by the initial learning rate for the Shule River Basin. Model testing under abrupt changes in runoff suggested that climatic factors could have different hydrological impacts, meanwhile, precipitation and heavy precipitation R95p had the greatest impacts on the hydrological process in the study area, followed by temperature, during the entire study period.

Key words: climate change, machine learning, cryospheric watersheds, hydrological process simulation, parameterization

中图分类号:

P338

宋轩宇, 许民, 康世昌, 孙立平. 基于机器学习的冰冻圈典型流域水文过程模拟研究[J]. 地学前缘, 2023, 30(4): 451-469.

SONG Xuanyu, XU Min, KANG Shichang, SUN Liping. Modeling of hydrological processes in cryospheric watersheds based on machine learning[J]. Earth Science Frontiers, 2023, 30(4): 451-469.

图/表 17

图1 研究区位置

Fig.1 Location (left) and geological sketch maps of the Yarkant (top right) and Shule (bottom right) river basins

表1 叶尔羌和疏勒河气象水文台站信息

Table 1 Information on hydrometeorological stations

站名	经度	纬度	海拔/m	时间段
莎车	75°23'E	37°77'N	1 231 m	1954-2015年
托勒	98°25'E	38°48'N	3 368 m	1970-2006年
卡群^*	76°90'E	37°98'N	1 370 m	1954-2015年
昌马堡^*	96°51'E	39°49'N	2 080 m	1970-2006年

图2 基于机器学习模型的水文过程模拟流程图

Fig.2 Flow chart of hydrological modeling based on machine learning

图3 LSTM层的存储单元结构

Fig.3 Diagram of a LSTM unit

图4 气温、降水、极端降水总量和径流深年际变化 (气象数据引自文献[38]) a-叶尔羌河流域;b-疏勒河流域。

Fig.4 Annual temperature, precipitation, R95p and runoff variations in the Yarkant (a) and Shule (b) river basins. Meteorological data from [38].

图5 气温、降水、极端降水总量和径流深的年内分布 (气象数据引自文献[38]) a-叶尔羌河流域; b-疏勒河流。

Fig.5 Monthly temperature, precipitation, R95p and runoff in the Yarkant (a) and Shule (b) river basins. Meteorological data from [38].

图6 叶尔羌河疏勒河流域月气温、降水、极端降水总量与径流深相关性 a-气温与径流深;b-降水与径流深;c-极端降水总量与径流深。

Fig.6 Correlation relationships between monthly temperature, precipitation, R95p and runoff in the study area

图7 叶尔羌河流域和疏勒河流域水文过程模拟结果

Fig.7 Hydrological modeling results for the study area using 6 machine learning algorithms

图8 6种机器学习模型对叶尔羌河流域水文过程模拟的泰勒图

Fig.8 Taylor diagrams to compare 6 machine learning algorithms in hydrological modeling of the Yarkant River Basin during training (a) and model testing (b)

图9 6种机器学习模型对疏勒河流域水文过程模拟的泰勒图

Fig.9 Taylor diagrams to compare 6 machine learning algorithms in hydrological modeling of the Shule River Basin during training (a) and model testing (b)

表2 机器学习模型的模拟精度评价

Table 2 Accuracy evaluation of machine learning models

方法	叶尔羌河流域						疏勒河流域
	训练期			验证期			训练期			验证期
	NSE	RMSE/mm	R	NSE	RMSE/mm	R	NSE	RMSE/mm	R	NSE	RMSE/mm	R
BP	0.71	7.19	0.84	0.69	7.92	0.83	0.82	2.85	0.91	0.81	4.28	0.92
GRNN	0.71	7.18	0.84	0.58	9.13	0.76	0.74	3.41	0.89	0.72	5.19	0.90
RBF	0.71	7.15	0.84	0.67	8.18	0.82	0.85	2.60	0.92	0.79	4.43	0.91
SVR	0.70	7.35	0.84	0.65	8.41	0.81	0.85	2.59	0.92	0.78	4.62	0.92
GA-BP	0.71	7.16	0.84	0.69	7.85	0.83	0.84	2.66	0.92	0.82	4.18	0.93
LSTM	0.90	4.24	0.95	0.88	4.80	0.94	0.85	2.57	0.92	0.72	5.17	0.91

图10 实测月径流与机器学习模型模拟结果 a-叶尔羌河流域; b-疏勒河流。

Fig.10 Comparison of average monthly runoffs between the measured values and machine learning results for the Yarkant (a) and Shule (b) river basins

图11 不同机器学习方法模拟的水文过程频率曲线 a、b-叶尔羌河流域; c、d-疏勒河流。

Fig.11 Runoff frequency curves using different machine learning algorithms for the Yarkant (a, b) and Shule (c,d) river basins

图12 损失函数图 a-f-叶尔羌河流域;g-l-疏勒河流域。

Fig.12 Plots of loss function vs. iterations under different parameterization schemes in training and testing runs. (a-f) Yarkant River Basin; (g-l) Shule River Basin.

表3 LSTM 模型参数化方案

Table 3 Parameterization schemes in LSTM models

参数	叶尔羌河流域	疏勒河流域
初始学习率	0.005	0.005
学习递减速率	0.9	0.9
优化器	Adam	Adam
批大小	12	12
迭代次数	25	25
LSTM层数	2	1
第一隐含层单元数目	100	100
第二隐含层单元数目	200

图13 年平均径流M-K突变检验 a-叶尔羌河流域;b-疏勒河流域。

Fig.13 Model testing under abrupt change in mean annual runoff in the study area

表4 气温、降水和极端降水总量对水文过程变化的贡献

Table 4 Percent contributions of temperature, precipitation and heavy precipitation R95p to runoff changes

参数	叶尔羌河流域						疏勒河流域
	1954-2015年		1954-1998年		1999-2015年		1970-2006年		1970-1998年		1999-2006年
	SR	MLR	SR	MLR	SR	MLR	SR	MLR	SR	MLR	SR	MLR
t	1%	2%	2%	2%	80%	80%	17%	11%	6%	6%	11%	11%
p	49%	49%	50%	50%	19%	19%	66%	66%	54%	54%	44%	44%
R95p	50%	49%	48%	48%	1%	1%	17%	23%	40%	40%	45%	45%

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基于机器学习的冰冻圈典型流域水文过程模拟研究

Modeling of hydrological processes in cryospheric watersheds based on machine learning

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