CN115293037A - Hydrodynamic modeling method of river-lake composite system based on machine learning - Google Patents

Hydrodynamic modeling method of river-lake composite system based on machine learning Download PDF

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CN115293037A
CN115293037A CN202210926817.6A CN202210926817A CN115293037A CN 115293037 A CN115293037 A CN 115293037A CN 202210926817 A CN202210926817 A CN 202210926817A CN 115293037 A CN115293037 A CN 115293037A
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lake
flow
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黄绳
夏军
王月玲
佘敦先
王纲胜
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Wuhan University WHU
Institute of Geographic Sciences and Natural Resources of CAS
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Abstract

The embodiment of the application provides a hydrodynamic modeling method of a river and lake composite system based on machine learning, wherein the boundary condition of a one-dimensional hydrodynamic model provides input for the machine learning; the output of the machine learning provides complex boundary conditions and downstream boundary conditions at river and lake junctions for the one-dimensional hydrodynamic model. The method improves the operating efficiency of hydrodynamic simulation of the river and lake composite system, can quickly calculate the water level and the flow of the important section position of the river and lake composite system, simultaneously reduces the requirement on terrain data, does not need to input complex lake terrain elevation survey data, and can effectively improve the water level simulation precision of the section near the downstream boundary.

Description

基于机器学习的河湖复合系统水动力建模方法Hydrodynamic modeling method of river-lake composite system based on machine learning

技术领域technical field

本申请涉及水动力建模领域,也可用于机器学习领域,具体涉及一种基于机器学习的河湖复合系统水动力建模方法。This application relates to the field of hydrodynamic modeling, and can also be used in the field of machine learning, and specifically relates to a method for hydrodynamic modeling of river-lake composite systems based on machine learning.

背景技术Background technique

机器学习是目前研究流域水文模拟的重要工具。在运用机器学习进行水文建模时,一般将流域内降雨或者上游流量作为输入,下游水位或流量作为输出。根据实测数据对机器学习模型的参数进行训练,完成参数训练的模型可以模拟和预测流域内河流或湖泊的水位和流量。机器学习是一种运行速度快、精度高的水文水动力模拟方法,但往往只能依次对单一站点进行模拟,难以对大范围河段各个断面的水文水动力要素进行建模。Machine learning is an important tool for current research on watershed hydrological simulation. When using machine learning for hydrological modeling, the rainfall or upstream flow in the watershed is generally used as input, and the downstream water level or flow is used as output. The parameters of the machine learning model are trained according to the measured data, and the model after parameter training can simulate and predict the water level and flow of rivers or lakes in the watershed. Machine learning is a hydrological and hydrodynamic simulation method with high speed and high precision. However, it can only simulate a single site sequentially, and it is difficult to model the hydrological and hydrodynamic elements of each section of a large-scale river section.

河流湖泊复合系统水动力数值模拟可以建立一维、二维或三维物理水动力模型。选择不同的模型维度本质上是求解精度和计算代价之间的权衡,同时也受到数据可用性的限制。对于长河段长时段的河流模拟,采用沿河道流程方向的一维模型有助于大幅减小计算量。湖泊区域由于水平方向的长度和宽度都比较大,不能用一维模型概化,往往采用沿水深方向平均的二维模型或者三维模型。但是对于大型湖泊而言,构建二维或三维模型需要的高分辨率水深测量数据或数字高程资料难以获取,同时还存在模型运行效率低、耦合边界处水位流量需要反复迭代且容易计算失稳等问题。在保证计算精度的前提下,合理概化湖泊模型以降低计算成本,是模拟河流湖泊复合系统水文水动力过程面临的一项重大挑战。此外,在没有水利工程调控的变化环境中,河湖复合系统的下游边界条件是未知的。通常采用的水位—流量关系曲线方法受到地形、植被等多种因素的干扰往往不太稳定,容易影响河湖复合系统水动力模拟的精度。One-dimensional, two-dimensional or three-dimensional physical hydrodynamic models can be established for the hydrodynamic numerical simulation of the composite system of rivers and lakes. Choosing different model dimensions is essentially a trade-off between solution accuracy and computational cost, and is also limited by data availability. For river simulations with long stretches and long periods of time, the use of a one-dimensional model along the flow direction of the river can help greatly reduce the amount of calculation. Due to the relatively large length and width in the horizontal direction, the lake area cannot be generalized by a one-dimensional model, and a two-dimensional or three-dimensional model averaged along the water depth direction is often used. However, for large lakes, it is difficult to obtain the high-resolution bathymetry data or digital elevation data required to build a 2D or 3D model. At the same time, there are also problems such as low model operation efficiency, repeated iterations of water level and flow at the coupling boundary, and easy calculation instability. question. On the premise of ensuring the calculation accuracy, it is a major challenge to simulate the hydrological and hydrodynamic process of the river-lake complex system by rationally generalizing the lake model to reduce the calculation cost. Furthermore, the downstream boundary conditions of river-lake complex systems are unknown in a changing environment without hydraulic engineering regulation. The commonly used water level-discharge relationship curve method is often not stable due to the interference of various factors such as terrain and vegetation, and it is easy to affect the accuracy of hydrodynamic simulation of river-lake complex systems.

机器学习具备在非线性系统中探索隐式关系的优势,耦合机器学习和水动力方法可能为河湖复合系统水动力建模中复杂问题的解决提供新的思路。实际应用和深入研究表明,目前关于机器学习和水动力的河湖复合系统建模方法存在一些缺点:(1)纯机器学习方法的计算站点有限,难以对整个研究区域的各个单元同时建模;(2)纯水动力方法计算耗时长,并且对地形数据要求较高,尤其是湖泊部分;(3)用水动力模型驱动机器学习进行替代建模的方式作用有限,需要根据地形变化等经常更新参数,可扩展性不强,没有充分结合机器学习和水动力方法的优势。Machine learning has the advantage of exploring implicit relationships in nonlinear systems. Coupling machine learning and hydrodynamic methods may provide new ideas for solving complex problems in hydrodynamic modeling of river-lake complex systems. Practical applications and in-depth research have shown that there are some shortcomings in the current modeling methods of river-lake complex systems related to machine learning and hydrodynamics: (1) The calculation sites of pure machine learning methods are limited, and it is difficult to model each unit of the entire study area at the same time; (2) The pure hydrodynamic method takes a long time to calculate and has high requirements for terrain data, especially for lakes; (3) The hydrodynamic model-driven machine learning method for alternative modeling is limited, and parameters need to be updated frequently according to terrain changes, etc. , the scalability is not strong, and the advantages of machine learning and hydrodynamic methods are not fully combined.

发明内容Contents of the invention

针对现有技术中的问题,本申请提供一种基于机器学习的河湖复合系统水动力建模方法,能够有效地提高河湖复合系统水动力模拟的运行效率。Aiming at the problems in the prior art, the present application provides a machine learning-based hydrodynamic modeling method of the river-lake composite system, which can effectively improve the operating efficiency of the river-lake composite system hydrodynamic simulation.

为了解决上述问题中的至少一个,本申请提供以下技术方案:In order to solve at least one of the above problems, the application provides the following technical solutions:

本申请提供一种基于机器学习的河湖复合系统水动力建模方法,包括:This application provides a machine learning-based hydrodynamic modeling method for river-lake composite systems, including:

收集研究区域上游边界、侧向边界和河湖交汇位置站点的流量资料,以及下游边界站点的水位资料;Collect flow data at the upstream boundary, lateral boundary, and junctions of rivers and lakes in the study area, as well as water level data at downstream boundary stations;

根据上游边界站点的流量和可能影响河湖之间水量交换的侧向支流站点流量,采用机器学习方法训练和预测河湖交汇位置的流量;According to the flow of the upstream boundary site and the flow of the lateral tributary site that may affect the water exchange between the river and the lake, the machine learning method is used to train and predict the flow of the river and lake intersection;

根据上游和侧向边界站点的流量,以及河湖之间交换的流量,采用机器学习方法训练和预测下游边界的水位;Using machine learning methods to train and predict water levels at downstream boundaries based on flows at upstream and lateral boundary stations, as well as flows exchanged between rivers and lakes;

根据河道地形资料对研究河段进行离散化,在河道上每隔一段距离划分一个断面,整理断面上的岸边起点距和高程数据;Discretize the researched river section according to the topographic data of the river channel, divide a section at intervals on the river channel, and sort out the bank start distance and elevation data on the section;

将各断面的初始流量赋值为上游边界第一天的流量,初始水位根据下游边界第一天的水位和河道坡降进行线性插值计算;The initial flow of each section is assigned as the flow of the first day of the upstream boundary, and the initial water level is calculated by linear interpolation according to the water level of the first day of the downstream boundary and the slope of the river;

离散圣维南方程组,并输入各河道断面初始和边界条件进行河道一维水动力计算。Discrete Saint-Venant's equations, and input the initial and boundary conditions of each channel section for one-dimensional hydrodynamic calculation of the channel.

进一步地,所述采用机器学习方法训练和预测河湖交汇位置的流量,包括:Further, the use of machine learning methods to train and predict the flow at the intersection of rivers and lakes includes:

采用的具体计算范式为:The specific calculation paradigm adopted is:

Figure BDA0003779856860000021
Figure BDA0003779856860000021

其中,

Figure BDA0003779856860000022
是第j个湖泊与河流交换的水量;Qin是上游来水量;QB是河流旁侧支流流量,且第j个湖泊与河流交换的水量一共受到i条河流支流的影响;
Figure BDA0003779856860000023
是第j个湖泊自身承接的支流流量,且一共有k条湖泊支流;f(g)代表预设机器学习方法。in,
Figure BDA0003779856860000022
is the amount of water exchanged between the j-th lake and the river; Q in is the inflow of upstream water; Q B is the flow rate of the side tributaries of the river, and the amount of water exchanged between the j-th lake and the river is affected by the tributaries of the i river;
Figure BDA0003779856860000023
is the tributary flow undertaken by the jth lake itself, and there are k lake tributaries in total; f(g) represents the default machine learning method.

进一步地,所述采用机器学习方法训练和预测下游边界的水位,包括:Further, said adopting machine learning method to train and predict the water level of the downstream boundary includes:

采用的下游边界水位具体计算范式如下:The specific calculation paradigm of the downstream boundary water level adopted is as follows:

Figure BDA0003779856860000031
Figure BDA0003779856860000031

其中,Z是下游边界水位;Qin是上游来水量;QB是河流旁侧支流流量;QL是湖泊与河流交换的水量;m和n分别是与河流相连的支流和通江湖泊个数;f(g)代表预设机器学习方法。Among them, Z is the water level of the downstream boundary; Q in is the inflow of upstream water; Q B is the flow of tributaries beside the river; Q L is the water exchange volume between lakes and rivers; m and n are the number of tributaries connected to the river and the number of lakes connected to the river ; f(g) represents the preset machine learning method.

由上述技术方案可知,本申请提供一种基于机器学习的河湖复合系统水动力建模方法,能够提高河湖复合系统水动力模拟的运行效率,快速计算河湖复合系统重要断面位置的水位和流量,同时降低了对地形资料的要求,不需要输入复杂的湖泊地形高程勘测数据,而且能够有效提高下游边界附近断面的水位模拟精度。It can be seen from the above technical solutions that this application provides a machine learning-based hydrodynamic modeling method for river-lake composite systems, which can improve the operation efficiency of hydrodynamic simulation of river-lake composite systems, and quickly calculate the water level and At the same time, it reduces the requirements for topographic data, does not need to input complex lake topographic elevation survey data, and can effectively improve the accuracy of water level simulation of sections near the downstream boundary.

附图说明Description of drawings

为了更清楚地说明本申请实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are For some embodiments of the present application, those of ordinary skill in the art can also obtain other drawings based on these drawings without creative effort.

图1为本申请实施例中的基于机器学习的河湖复合系统水动力建模方法的流程示意图;Fig. 1 is the schematic flow chart of the hydrodynamic modeling method of river-lake composite system based on machine learning in the embodiment of the present application;

图2为本申请一具体实施例中主要站点的实测和模拟的水位、流量对比示意图;Fig. 2 is the measured and simulated water level, flow comparison schematic diagram of main site in a specific embodiment of the present application;

图3为本申请一具体实施例中实测和模拟的下游边界水位对比示意图。Fig. 3 is a schematic diagram of comparison of measured and simulated downstream boundary water levels in a specific embodiment of the present application.

具体实施方式Detailed ways

为使本申请实施例的目的、技术方案和优点更加清楚,下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整的描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

本申请技术方案中对数据的获取、存储、使用、处理等均符合国家法律法规的相关规定。The acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

为了能够有效地提高河湖复合系统水动力模拟的运行效率,本申请提供一种基于机器学习的河湖复合系统水动力建模方法的实施例,参见图1,所述基于机器学习的河湖复合系统水动力建模方法具体包含有如下内容:In order to effectively improve the operating efficiency of the hydrodynamic simulation of the river-lake composite system, the present application provides an embodiment of a machine learning-based hydrodynamic modeling method for the river-lake composite system, see Figure 1, the machine-learning-based river-lake The hydrodynamic modeling method of composite system specifically includes the following contents:

步骤1,水文资料收集:收集研究区域上游边界、侧向边界和河湖交汇位置站点的流量资料,以及下游边界站点的水位资料。Step 1, Hydrological data collection: collect the flow data of the upstream boundary, lateral boundary and the intersection of rivers and lakes in the study area, and the water level data of the downstream boundary stations.

具体地,根据《水文年鉴》等资料源,收集长江中游(包括长江干流、洞庭湖、鄱阳湖、汉江等)2007-2016年上游边界流量(宜昌站流量)、河流支流流量(汉江仙桃站流量)、湖泊支流流量(洞庭湖的石门站、桃江站、桃源站和湘潭站流量,鄱阳湖的外洲站、李家渡站、梅港站、虎山站、虬津站、万家埠站和渡峰坑站流量)、河湖交汇点流量(洞庭湖的城陵矶站流量,鄱阳湖的湖口站流量)和下游边界水位(大通站水位),所有流量或水文资料均为日尺度水文数据。Specifically, according to the "Hydrological Yearbook" and other data sources, the upstream boundary flow (Yichang station flow), river tributary flow (Hanjiang Xiantao station flow ), lake tributary flow (Shimen Station, Taojiang Station, Taoyuan Station and Xiangtan Station flow in Dongting Lake, Waizhou Station, Lijiadu Station, Meigang Station, Hushan Station, Qiujin Station, Wanjiabu Station and Ferry Station in Poyang Lake Fengkeng station flow), river and lake confluence point flow (Dongting Lake’s Chenglingji station flow, Poyang Lake’s Hukou station flow) and downstream boundary water level (Datong station water level), all flow or hydrological data are daily-scale hydrological data .

步骤2,河湖交换水量计算:根据上游边界站点的流量和可能影响河湖之间水量交换的侧向支流站点流量,采用机器学习方法训练和预测河湖交汇位置的流量。具体计算范式如下:Step 2. Calculation of water exchange between rivers and lakes: According to the flow of upstream boundary sites and the flow of lateral tributary sites that may affect the water exchange between rivers and lakes, machine learning methods are used to train and predict the flow at the intersection of rivers and lakes. The specific calculation paradigm is as follows:

Figure BDA0003779856860000041
Figure BDA0003779856860000041

其中,

Figure BDA0003779856860000042
是第j个湖泊与河流交换的水量;Qin是上游来水量,即宜昌站流量;QB是河流旁侧支流流量,且第j个湖泊与河流交换的水量一共受到i条河流支流的影响;
Figure BDA0003779856860000043
是第j个湖泊自身承接的支流流量,且一共有k条湖泊支流;RNN(g)代表一种称为循环神经网络的机器学习方法。in,
Figure BDA0003779856860000042
is the amount of water exchanged between the jth lake and the river; Q in is the amount of upstream water, that is, the flow of Yichang station; Q B is the flow rate of the tributaries beside the river, and the amount of water exchanged between the jth lake and the river is affected by the tributaries of the i river ;
Figure BDA0003779856860000043
is the tributary flow undertaken by the jth lake itself, and there are k lake tributaries in total; RNN(g) represents a machine learning method called a recurrent neural network.

将2007-2013年河湖交汇处的流量作为机器学习的输出,上游边界流量(宜昌站流量)、相应湖泊的支流流量(洞庭湖的石门站、桃江站、桃源站和湘潭站流量,鄱阳湖的外洲站、李家渡站、梅港站、虎山站、虬津站、万家埠站和渡峰坑站流量)和能够影响该河湖交汇处的上游河流支流流量(例如,鄱阳湖和长江干流交汇处的湖口站受到汉江流量的影响)作为机器学习的输入,训练机器学习模型的参数。用训练好参数的RNN模型预测2014-2016年河湖交汇处的流量(洞庭湖的城陵矶站流量,鄱阳湖的湖口站流量)。Taking the flow at the confluence of rivers and lakes from 2007 to 2013 as the output of machine learning, the upstream boundary flow (Yichang Station flow), the tributary flow of the corresponding lake (Dongting Lake’s Shimen Station, Taojiang Station, Taoyuan Station and Xiangtan Station flow, Poyang Lake The flow of Waizhou Station, Lijiadu Station, Meigang Station, Hushan Station, Qiujin Station, Wanjiabu Station and Dufengkeng Station) and the flow of upstream river tributaries that can affect the confluence of the river and lake (for example, Poyang Lake and The Hukou Station at the confluence of the main stream of the Yangtze River is affected by the flow of the Han River) as the input of machine learning to train the parameters of the machine learning model. Use the RNN model with trained parameters to predict the flow at the confluence of rivers and lakes from 2014 to 2016 (the flow at Chenglingji Station in Dongting Lake, the flow at Hukou Station in Poyang Lake).

步骤3,下游边界水位计算:根据上游和侧向边界站点的流量,以及河湖之间交换的流量,采用机器学习方法训练和预测下游边界的水位。具体计算范式如下:Step 3, Downstream Boundary Water Level Calculation: According to the flow of upstream and lateral boundary stations, and the flow exchanged between rivers and lakes, machine learning methods are used to train and predict the water level of the downstream boundary. The specific calculation paradigm is as follows:

Figure BDA0003779856860000051
Figure BDA0003779856860000051

其中,Z是下游边界水位,即大通站水位;Qin是上游来水量,即宜昌流量;QB是河流旁侧支流流量,即汉江仙桃站流量;QL是湖泊与河流交换的水量;m和n分别是与河流相连的支流和通江湖泊个数,分别是1和2;RNN(g)代表一种称为循环神经网络的机器学习方法。Among them, Z is the downstream boundary water level, that is, the water level of Datong Station; Q in is the upstream water flow, that is, the Yichang flow; Q B is the flow of the tributaries beside the river, that is, the Hanjiang Xiantao Station flow; Q L is the water exchange volume between the lake and the river; m and n are the number of tributaries connected to the river and the number of lakes connected to the river, which are 1 and 2 respectively; RNN(g) represents a machine learning method called a recurrent neural network.

将2007-2013年下游边界站点的水位(大通站水位)作为机器学习模型的目标变量,上游边界流量(宜昌站流量)、所有上游河流支流流量(汉江仙桃站流量)和湖泊进出河流的流量(城陵矶站流量和湖口站流量)作为机器学习模型的输入,训练机器学习模型的参数。用训练好参数的RNN模型预测2014-2016年下游边界站点的水位(大通站的水位)。Taking the water level of the downstream boundary station (Datong station water level) from 2007 to 2013 as the target variable of the machine learning model, the upstream boundary flow (Yichang station flow), all upstream river tributary flows (Hanjiang Xiantao station flow) and the lake flow into and out of the river ( Chenglingji station flow and Hukou station flow) are used as the input of the machine learning model to train the parameters of the machine learning model. Use the RNN model with trained parameters to predict the water level at the downstream boundary station (the water level at Datong Station) from 2014 to 2016.

步骤4,划分河道断面:根据河道地形资料对研究河段进行离散化,在河道上每隔一段距离划分一个断面。共划分出535个断面,断面间距多为1-5千米。整理这些断面上的岸边起点距和高程数据。Step 4. Divide the river section: discretize the research section according to the topographic data of the river, and divide a section at intervals on the river. A total of 535 sections are divided, and the interval between sections is mostly 1-5 kilometers. Organize the shore start distance and elevation data on these sections.

步骤5,初始条件设置:将535个断面的初始流量赋值为上游边界宜昌站第一天(2014年1月1日)的流量,初始水位根据下游边界大通站第一天(2014年1月1日)的水位和河道坡降进行线性插值计算;Step 5, initial condition setting: Assign the initial flow of 535 sections as the flow of the first day (January 1, 2014) at the Yichang Station on the upstream boundary, and the initial water level is based on the first day of the Datong Station on the downstream boundary (January 1, 2014). Day) of water level and river slope for linear interpolation calculation;

步骤6,离散如下形式的Saint-Venant方程组(圣维南方程组,即一维水动力模型的基本方程):Step 6, discretizing the Saint-Venant equations in the following form (Saint-Venant equations, the basic equations of the one-dimensional hydrodynamic model):

Figure BDA0003779856860000052
Figure BDA0003779856860000052

式中:A为面积,m;t为时间,s;Q为流量,m3/s;x为流程,m;q为单位流程上的侧向流,正值表示流入,m2/s;g为重力加速度,取9.81m/s2;B为水面宽度,m;Z为水位,m;n为糙率,无量纲;R为湿周,m。In the formula: A is the area, m; t is the time, s; Q is the flow rate, m 3 /s; x is the flow, m; q is the lateral flow on the unit flow, a positive value means inflow, m 2 /s; g is the acceleration of gravity, taken as 9.81m/s 2 ; B is the width of the water surface, in m; Z is the water level, in m; n is the roughness, dimensionless; R is the wetted circumference, in m.

根据步骤1-3得到的边界条件和步骤5得到的初始条件求解一维水动力模型圣维南方程组。主要河湖站点的水位和流量模拟结果如图2所示。下游边界水位的模拟值和实测值如图3所示。该方法较好地模拟了河湖复合系统的水位和流量,适合用于河湖复合系统的水动力建模。此外,该方法能够在9.4分钟内模拟研究区域时长1年的水位和流量,与传统一二维耦合水动力模型在同区域同时长模拟花费6小时相比,提高了38倍的河湖复合系统水动力模型运行效率(Xijun Lai,Jiahu Jiang,Qiuhua Liang,Qun Huang,2013,Large-scale hydrodynamic modeling of the middle Yangtze River Basin with complexriver–lake interactions,Journal of Hydrology,492,pp.228-243.)。According to the boundary conditions obtained in steps 1-3 and the initial conditions obtained in step 5, the one-dimensional hydrodynamic model Saint-Venant equations are solved. The water level and discharge simulation results of major river and lake stations are shown in Fig. 2. The simulated and measured values of the downstream boundary water level are shown in Fig. 3. This method simulates the water level and discharge of the river-lake complex system well, and is suitable for hydrodynamic modeling of the river-lake complex system. In addition, the method can simulate the water level and flow of the study area for a period of 1 year in 9.4 minutes, which is 38 times higher than that of the traditional one-dimensional coupled hydrodynamic model in the same area, which takes 6 hours to simulate at the same time. Operation efficiency of hydrodynamic model (Xijun Lai, Jiahu Jiang, Qiuhua Liang, Qun Huang, 2013, Large-scale hydrodynamic modeling of the middle Yangtze River Basin with complexriver–lake interactions, Journal of Hydrology, 492, pp.228-243.) .

从上述描述可知,本申请实施例提供的基于机器学习的河湖复合系统水动力建模方法,能够通过灵活建模提高河湖复合系统水动力模拟的运行效率,快速计算河湖复合系统重要断面位置的水位和流量,同时降低了对地形资料的要求,不需要输入复杂的湖泊地形高程勘测数据,而且能够有效提高下游边界附近断面的水位模拟精度。From the above description, it can be seen that the machine learning-based hydrodynamic modeling method of the river-lake composite system provided by the embodiment of the present application can improve the operation efficiency of the hydrodynamic simulation of the river-lake composite system through flexible modeling, and quickly calculate the important sections of the river-lake composite system At the same time, it reduces the requirements for topographic data, does not need to input complex lake topographic elevation survey data, and can effectively improve the accuracy of water level simulation of sections near the downstream boundary.

本发明中应用了具体实施例对本发明的原理及实施方式进行了阐述,以上实施例的说明只是用于帮助理解本发明的方法及其核心思想;同时,对于本领域的一般技术人员,依据本发明的思想,在具体实施方式及应用范围上均会有改变之处,综上所述,本说明书内容不应理解为对本发明的限制。In the present invention, specific examples have been applied to explain the principles and implementation methods of the present invention, and the descriptions of the above examples are only used to help understand the method of the present invention and its core idea; meanwhile, for those of ordinary skill in the art, according to this The idea of the invention will have changes in the specific implementation and scope of application. To sum up, the contents of this specification should not be construed as limiting the present invention.

Claims (3)

1.一种基于机器学习的河湖复合系统水动力建模方法,其特征在于,所述方法包括:1. A method for modeling hydrodynamics of a composite system of rivers and lakes based on machine learning, characterized in that the method comprises: 收集研究区域上游边界、侧向边界和河湖交汇位置站点的流量资料,以及下游边界站点的水位资料;Collect flow data at the upstream boundary, lateral boundary, and junctions of rivers and lakes in the study area, as well as water level data at downstream boundary stations; 根据上游边界站点的流量和可能影响河湖之间水量交换的侧向支流站点流量,采用机器学习方法训练和预测河湖交汇位置的流量;According to the flow of the upstream boundary site and the flow of the lateral tributary site that may affect the water exchange between the river and the lake, the machine learning method is used to train and predict the flow of the river and lake intersection; 根据上游和侧向边界站点的流量,以及河湖之间交换的流量,采用机器学习方法训练和预测下游边界的水位;Using machine learning methods to train and predict water levels at downstream boundaries based on flows at upstream and lateral boundary stations, as well as flows exchanged between rivers and lakes; 根据河道地形资料对研究河段进行离散化,在河道上每隔一段距离划分一个断面,整理断面上的岸边起点距和高程数据;Discretize the researched river section according to the topographic data of the river channel, divide a section at intervals on the river channel, and sort out the bank start distance and elevation data on the section; 将各断面的初始流量赋值为上游边界第一天的流量,初始水位根据下游边界第一天的水位和河道坡降进行线性插值计算;The initial flow of each section is assigned as the flow of the first day of the upstream boundary, and the initial water level is calculated by linear interpolation according to the water level of the first day of the downstream boundary and the slope of the river; 离散圣维南方程组,并输入各河道断面初始和边界条件进行河道一维水动力计算。Discrete Saint-Venant's equations, and input the initial and boundary conditions of each channel section for one-dimensional hydrodynamic calculation of the channel. 2.根据权利要求1所述的基于机器学习的河湖复合系统水动力建模方法,其特征在于,所述采用机器学习方法训练和预测河湖交汇位置的流量,包括:2. The hydrodynamic modeling method of river-lake complex system based on machine learning according to claim 1, characterized in that, said adopting machine learning method to train and predict the flow at the intersection of river and lake includes: 采用的具体计算范式为:The specific calculation paradigm adopted is:
Figure FDA0003779856850000011
Figure FDA0003779856850000011
其中,
Figure FDA0003779856850000012
是第j个湖泊与河流交换的水量;Qin是上游来水量;QB是河流旁侧支流流量,且第j个湖泊与河流交换的水量一共受到i条河流支流的影响;
Figure FDA0003779856850000013
是第j个湖泊自身承接的支流流量,且一共有k条湖泊支流;f(g)代表预设机器学习方法。
in,
Figure FDA0003779856850000012
is the amount of water exchanged between the j-th lake and the river; Q in is the inflow of upstream water; Q B is the flow rate of the side tributaries of the river, and the amount of water exchanged between the j-th lake and the river is affected by the tributaries of the i river;
Figure FDA0003779856850000013
is the tributary flow undertaken by the jth lake itself, and there are k lake tributaries in total; f(g) represents the default machine learning method.
3.根据权利要求1所述的基于机器学习的河湖复合系统水动力建模方法,其特征在于,所述采用机器学习方法训练和预测下游边界的水位,包括:3. the hydrodynamic modeling method of river-lake composite system based on machine learning according to claim 1, is characterized in that, described adopting machine learning method to train and predict the water level of downstream boundary, comprising: 采用的下游边界水位具体计算范式如下:The specific calculation paradigm of the downstream boundary water level adopted is as follows:
Figure FDA0003779856850000014
Figure FDA0003779856850000014
其中,Z是下游边界水位;Qin是上游来水量;QB是河流旁侧支流流量;QL是湖泊与河流交换的水量;m和n分别是与河流相连的支流和通江湖泊个数;f(g)代表预设机器学习方法。Among them, Z is the water level of the downstream boundary; Q in is the inflow of upstream water; Q B is the flow of tributaries beside the river; Q L is the water exchange volume between lakes and rivers; m and n are the number of tributaries connected to the river and the number of lakes connected to the river ; f(g) represents the preset machine learning method.
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Publication number Priority date Publication date Assignee Title
CN115496015A (en) * 2022-11-18 2022-12-20 珠江水利委员会珠江水利科学研究院 A Hydrodynamic Analysis and Decision-making Method Based on Discharge Gradient Variation
CN115496015B (en) * 2022-11-18 2023-02-28 珠江水利委员会珠江水利科学研究院 Hydrodynamic analysis decision method based on flow gradient change

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