汉江流域安康站日径流预测的LSTM模型初步研究

doi:10.18306/dlkxjz.2020.04.010

地理科学进展 ›› 2020, Vol. 39 ›› Issue (4): 636-642.doi: 10.18306/dlkxjz.2020.04.010

汉江流域安康站日径流预测的LSTM模型初步研究

胡庆芳¹, 曹士圯¹, 杨辉斌^1,², 王银堂¹, 李伶杰¹, 王立辉²

^1. 南京水利科学研究院水文水资源与水利工程科学国家重点实验室,南京 210029
^2. 福州大学水利水电与港口工程系,福州 350116

收稿日期:2019-11-04 修回日期:2020-02-20 出版日期:2020-04-28 发布日期:2020-06-28
作者简介:胡庆芳(1981— ),男,博士、高工,研究方向为水文预测预报、水文遥感和水资源规划。E-mail: hqf_work@163.com
基金资助:
国家重点研发计划项目(2016YFC0400902);国家自然科学基金项目(51479118)

Daily runoff predication using LSTM at the Ankang Station, Hanjing River

HU Qingfang¹, CAO Shiyi¹, YANG Huibin^1,², WANG Yintang¹, LI Linjie¹, WANG Lihui²

^1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
^2. Department of Water Resources, Hydropower and PortEngineering, Fuzhou University, Fuzhou 350116, China

Received:2019-11-04 Revised:2020-02-20 Online:2020-04-28 Published:2020-06-28
Supported by:
National Key Research and Development Program of China(2016YFC0400902);National Natural Science Foundation of China(51479118)

摘要/Abstract

摘要：

论文基于2003—2014年水文资料,采用长短期记忆神经网络(Long-Short Term Memory,LSTM),构建了汉江上游安康站日径流预测模型,评价了不同输入条件下日径流预测的精度。结果表明：当预见期为1 d时,在仅以安康站前期日径流量作为输入的条件下,LSTM模型在训练期和检验期的效率系数分别达到0.68和0.74;如再将流域前期面雨量和上游石泉站前期日径流量加入LSTM网络作为输入变量,安康站日径流量预测效果将更好,训练期和检验期的效率系数最高可达到0.83和0.84,均方根误差也有显著削减,且对主要洪峰流量的预测能力也有一定提高。此外,LSTM可以有效避免过拟合等问题,具有较好的泛化性能。但当预见期从1 d延长至2、3 d时,LSTM的预测精度显著降低。

关键词: 长短期记忆神经网络, 日径流预测, 汉江流域, 安康站

Abstract:

Based on the hydrological data from 2003 to 2014, Long-Short Term Memory (LSTM) was used to construct a daily runoff prediction model for the Ankang discharge station in the upper reaches of the Hanjiang River. The accuracy of daily runoff prediction was evaluated under different input conditions. The result shows that when the foreseeing period is one day, the efficiency coefficient of the LSTM in the calibration period and the validation period can reach 0.68 and 0.74 respectively under the condition that only the previous daily runoff of the Ankang Station is used as input. When the previous areal rainfall of the catchment and the previous daily runoff of the upstream Shiquan Station were added to the LSTM model as input variables, the daily runoff prediction precision was improved. The efficiency coefficient of the training period and the validation period could reach 0.83 and 0.84, respectively. The root mean square error was also significantly reduced. The accuracy of the main flood peak flow forecasting also increased. The LSTM can effectively avoid the problem of over-fitting, and has better generalization performance. However, when the foreseeable period is extended from one day to two or three days, the performance of LSTM is significantly reduced.

Key words: long-short term memory, daily runoff forecast, Hanjiang River Basin, Ankang Station

胡庆芳, 曹士圯, 杨辉斌, 王银堂, 李伶杰, 王立辉. 汉江流域安康站日径流预测的LSTM模型初步研究[J]. 地理科学进展, 2020, 39(4): 636-642.

HU Qingfang, CAO Shiyi, YANG Huibin, WANG Yintang, LI Linjie, WANG Lihui. Daily runoff predication using LSTM at the Ankang Station, Hanjing River[J]. PROGRESS IN GEOGRAPHY, 2020, 39(4): 636-642.

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站点	最大值 /(m³/s)	最小值 /(m³/s)	平均值 /(m³/s)	标准差 /(m³/s)	变差系数
石泉	9770.0	3.6	315.2	628.9	2.0
安康	16800.0	0	588.7	965.8	1.6

预测方案	模型输入变量			PT/d
预测方案	安康站前期径流序列	安康站集水范围前期面雨量序列	安康站上游石泉站前期径流序列	PT/d
方案1	√			1~3
方案2		√		1~3
方案3			√	1~3
方案4	√	√		1~3
方案5	√		√	1~3
方案6		√	√	1~3
方案7	√	√	√	1~3

预测方案	精度指标	PT=1 d		PT=2 d		PT=3 d
预测方案	精度指标	训练期	检验期	训练期	检验期	训练期	检验期
方案1	NSE	0.68	0.74	0.43	0.58	0.34	0.47
方案1	RMSE/(m³/s)	529	403	711	511	763	573
方案2	NSE	0.65	0.66	0.54	0.54	0.39	0.43
方案2	RMSE/(m³/s)	554	456	638	534	734	594
方案3	NSE	0.64	0.72	0.46	0.60	0.36	0.54
方案3	RMSE/(m³/s)	566	419	694	499	755	532
方案4	NSE	0.77	0.77	0.58	0.62	0.43	0.49
方案4	RMSE/(m³/s)	456	373	610	487	713	559
方案5	NSE	0.72	0.76	0.49	0.62	0.39	0.54
方案5	RMSE/(m³/s)	496	381	669	484	735	532
方案6	NSE	0.79	0.81	0.61	0.65	0.43	0.51
方案6	RMSE/(m³/s)	430	347	590	463	712	548
方案7	NSE	0.83	0.84	0.62	0.67	0.43	0.56
方案7	RMSE/(m³/s)	384	316	581	454	708	525

汉江流域安康站日径流预测的LSTM模型初步研究

Daily runoff predication using LSTM at the Ankang Station, Hanjing River

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