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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (5): 1010-1018    DOI: 10.3785/j.issn.1008-973X.2021.05.022
土木工程、水利工程     
基于多水文模型预报能力分析的洪水预报
刘登嵩1(),葛路1,许月萍1,张善亮2,江衍铭1,*()
1. 浙江大学 建筑工程学院 水文与水资源工程研究所，浙江 杭州 310058
2. 中国电建集团华东勘测设计研究院有限公司，浙江 杭州 310058
Flood prediction by multi-hydrological models with forecasting ability analysis
Deng-song LIU1(),Lu GE1,Yue-ping XU1,Shan-liang ZHANG2,Yen-ming CHIANG1,*()
1. Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Power China HuaDong Engineering Co. Ltd, Hangzhou 310058, China
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摘要：

为了解决概念性和黑箱模型在小时尺度模拟效果对比中预见期选择的问题，提出利用滞后演算法求解概念性模型预报能力. 在两模型共同预报能力条件下，引入集对分析理论对比新安江模型、土壤水核算与演算（SMAR）模型、倒传递（BP）和Elman神经网络模型模拟效果. 概念性模型预报能力与流域面积成正相关且特定流域的预报能力相对固定，其中金华江和龙泉溪流域的预见期可以分别取第7、3 h；在相同预见期条件下，龙泉溪流域表现最好的是BP模型而最差的是Elman模型，金华江流域表现最好的是新安江模型而最差的是SMAR模型；引入集对分析方法可以从单指标转向更多指标进行综合评价，具有较好的适用性和高效性.
关键词： 黑箱模型;  概念性模型;  滞后演算法;  集对分析;  洪水预报    
Abstract:

The forecast ability of the conceptual model was evaluated by the lag and route method to solve the selection problem of appropriate forecast lead time for both black box and conceptual models with hourly time scale. With the same forecast lead time, the flood forecasting was carried out by using the Xin'an jiang model, soil moisture accounting and routing (SMAR) model, back propagation (BP) neural network and Elman neural network and the performance was analyzed according to the set pair analysis method. The forecast ability of the conceptual models was relatively stable in specific watersheds and was corresponding to the size of the watershed. According to the lag and route method, the forecast lead time values for Jinhua and Longquan watersheds were 7 hours and 3 hours, respectively. Results indicated that the BP model and the Elman model produced the best and the worst performance, respectively, for flood forecasting in Longquan basin under the same forecast lead time. Moreover, the Xin'an jiang model and the SMAR model produced the best and the worst performance, respectively, for flood forecasting in Jinhua basin. The set pair analysis method is able to carry out comprehensive evaluation from single criterion to multiple criteria, indicating that the method has better applicability and higher efficiency.
Key words: black-box model    conceptual model    lag and route method    set pair analysis    flood forecasting
收稿日期: 2020-07-22 出版日期: 2021-06-10
CLC:  TV 213  
基金资助: 浙江省重点研发计划择优委托资助项目（2021C03017）；国家重点研发计划政府间/港澳台重点专项资助项目（2016YFE0122100）；浙江省自然基金重点资助项目（LZ20E090001）
通讯作者: 江衍铭     E-mail: liuds@zju.edu.cn;chiangym@zju.edu.cn
作者简介: 刘登嵩（1995—），男，硕士，从事水文预报研究. orcid.org/0000-0002-7106-4218. E-mail： liuds@zju.edu.cn
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引用本文:

刘登嵩,葛路,许月萍,张善亮,江衍铭. 基于多水文模型预报能力分析的洪水预报[J]. 浙江大学学报(工学版), 2021, 55(5): 1010-1018.

Deng-song LIU,Lu GE,Yue-ping XU,Shan-liang ZHANG,Yen-ming CHIANG. Flood prediction by multi-hydrological models with forecasting ability analysis. Journal of ZheJiang University (Engineering Science), 2021, 55(5): 1010-1018.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.05.022    或    http://www.zjujournals.com/eng/CN/Y2021/V55/I5/1010

图 1  滞后演算法原理
图 2  龙泉溪新安江模型不同种群数量和迭代次数下的训练结果
评价指标 Ivf NSE Qer Ter
1级 ≤5 ≥0.9 ≥80 ≥80
2级 5~10 0.8~0.9 50~80 50~80
3级 10~50 0~0.8 0~50 0~50
表 1  评价指标各等级标准
图 3  研究区域龙泉溪、金华江站点及边界情况
图 4  龙泉溪流域不同河网演算滞时NSE结果
图 5  金华江流域不同河网演算滞时NSE结果
图 6  龙泉溪流域BP、Elman、SMAR和XAJ模型体积流量拟合效果比较
图 7  金华江流域BP、Elman、SMAR和XAJ模型体积流量拟合效果比较
流域 模型 Ivf NSE Qer Ter
龙泉溪 BP 1.46 0.96 66.67 100.00
Elman 21.46 0.88 44.44 66.67
SMAR 1.86 0.85 33.33 55.56
XAJ 4.58 0.93 55.56 66.67
金华江 BP 3.85 0.88 50.00 50.00
Elman 0.67 0.84 75.00 25.00
SMAR 14.88 0.67 50.00 12.50
XAJ 3.26 0.85 75.00 62.50
表 2  不同流域各模型不同评价指标
流域 模型 等级 联系数
龙泉溪 BP 1 0.72
Elman 3 0.55
SMAR 2 0.78
XAJ 2 0.86
金华江 BP 2 0.76
Elman 2 0.81
SMAR 3 0.40
XAJ 2 0.87
表 3  不同流域各模型集对分析结果
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