WO2022110582A1 - 一种基于基流分割和人工神经网络模型的径流预报方法 - Google Patents
一种基于基流分割和人工神经网络模型的径流预报方法 Download PDFInfo
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- the invention relates to the technical field of runoff forecasting, in particular to a runoff forecasting method based on base flow segmentation and an artificial neural network model.
- Runoff forecasting is an important part of water resources management and a challenging research topic, which has always been one of the most concerned issues in hydrological practice. Runoff forecasting can provide useful information for water resources management, and is of great significance to the optimal management and effective utilization of water resources, such as hydropower generation, drought and flood disaster assessment, water resource scheduling and water supply allocation. However, the forecast lead time of runoff forecast is long, and the precipitation forecast during the period has inherent uncertainty, so forecasting faces major challenges.
- LSTM Long Short-Term Memory
- LSTM Long Short-Term Memory Due to its complex network structure, it can process long-term time series data well, and this model is widely used in the field of medium and long-term hydrological forecasting. .
- the prediction accuracy of the LSTM model is good, its prediction accuracy is unstable, resulting in the extreme value prediction in the hydrological process not reaching a high standard, which in turn affects the subsequent water resources allocation work.
- the purpose of the present invention is to provide a runoff forecasting method based on base flow segmentation and artificial neural network model in view of the deficiencies of the existing model technology.
- the runoff is divided into two parts, and the two parts are used as the driving factors of the artificial neural network model to predict the medium and long-term runoff flow, so as to improve the accuracy of hydrological process simulation and extreme value forecasting.
- a runoff forecasting method based on base flow segmentation and artificial neural network model comprising the following steps:
- the digital filtering base flow segmentation method is used to decompose it into two parts: base flow and surface runoff.
- the calculation method is as follows:
- Q i is the runoff
- Q s(i) is the surface runoff
- i is the time step
- ⁇ is the decay coefficient, which ranges from 0.9 to 0.95.
- the base flow Q b(i) is calculated by the following formula:
- NSE Nash efficiency coefficient
- Bias% percentage deviation
- n is the total number of data.
- step (2) and (3) of the present invention are used as the predictors of the artificial neural network model to simulate the runoff, aiming to improve the fitting degree of the hydrological process in medium and long-term forecasting, and improve the annual maximum and minimum flow forecast accuracy.
- the present invention proposes a medium and long-term runoff prediction model based on the digital filtering base flow segmentation method and the artificial neural network model, thereby improving the accuracy and reliability of the runoff forecast.
- the present invention makes up for the deficiency of the single predictor artificial neural network model for the simulation of annual peak and valley values, and greatly improves the simulation accuracy.
- the present invention proposes a new medium and long-term forecasting method, which can reach the highest standard by comparing the precision division indicators of national forecasting items, and can provide scientific theoretical support for the water resources allocation plan of the basin.
- Fig. 1 is a schematic flow chart of the present invention
- Fig. 2 is the digital filtering method base stream segmentation result figure of a specific example of the present invention
- Fig. 3 is the actual measurement-simulation flow hydrograph of a specific example of the present invention.
- Fig. 4 is the actual measurement-simulation year maximum flow scatter diagram of a specific example of the present invention.
- Fig. 5 is the actual measurement-simulation year minimum flow scatter diagram of a specific example of the present invention.
- the artificial neural network model with base flow and surface runoff as input is used to simulate the hydrological process line of a hydrological station in a certain watershed, and the simulation effect is compared with the artificial neural network model of a single predictor (runoff sequence). .
- a medium and long-term runoff forecasting method based on base flow segmentation and artificial neural network model of the present invention includes the following steps:
- Data collection and processing Collect the measured flow series of hydrological stations from 1961 to 2000, and perform base flow segmentation on the runoff data by digital filtering method.
- the calculation formula is as follows:
- Q i is the runoff
- Q s(i) is the surface runoff
- i is the time step
- ⁇ is the decay coefficient, which ranges from 0.9 to 0.95.
- this method In order to correct the phase distortion, this method generally adopts forward-reverse-forward cubic filtering to obtain the baseflow and surface runoff sequences of hydrological stations, which can make the baseflow curve smoother, as shown in Figure 2.
- Model input and output The LSTM artificial neural network model is selected as an example model of the present invention.
- a single forecast factor (runoff) and two forecast factors (base flow and surface runoff) are used as model inputs respectively, and the model outputs are all monthly runoff data.
- Figure 3 shows the measured runoff at the hydrological site and the simulated hydrographs of the two schemes. From the results in the figure, the flow hydrograph obtained by the medium and long-term runoff forecasting method based on base flow segmentation and artificial neural network model is better than the simulation results of the artificial neural network forecasting model with a single predictor.
- Verification of the overall simulation effect of the model Substitute the simulation results obtained in step (3) into the evaluation index formula, and reflect the fitting effect of the model from the numerical results.
- the specific calculation equation is as follows:
- n is the total number of data.
- the method of the present invention has a great improvement.
- the NSE value in the test period is increased from 0.782 to 0.904, and the result of Bias% is also greatly improved (-0.353% to 0.195%).
- the certainty coefficient greater than 0.90 is the accuracy grade, and the Nash efficiency of this model is 0.904 during the verification period.
- the percentage deviation meets the index of less than 15%, indicating that the effect of this forecasting model is very good. , which can be used for the mid- and long-term runoff forecast of this hydrological site and the subsequent optimal allocation of water resources.
- Figures 4 and 5 are the scatter plots of the minimum and maximum flow in the measured and modeled years at the hydrological site, respectively. The results in the figure show that the forecast method based on the base flow segmentation and the LSTM model has a better fitting effect on the annual maximum and minimum monthly runoff values, which is greatly improved compared with the single forecast factor LSTM model.
- the above description is only implemented for the example of the present invention, and is not intended to limit the present invention.
- the predictors (base flow and surface runoff) in the present invention can be divided into base flow by digital filtering method according to the runoff sequence of different research areas.
- the neural network model can also perform model training and kernel function selection according to different research areas. Any modification, equivalent replacement, improvement, etc. made within the scope of the claims of the present invention shall fall within the protection scope of the present invention.
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Abstract
一种基于基流分割和人工神经网络模型的径流预报方法，在中长期径流预报中考虑基流分割问题，采用数字滤波基流分割法将径流分割成基流和地表径流两部分，并将这两部分作为人工神经网络模型的驱动因子预测中长期径流流量，采用所述方法可以有效提高水文过程模拟和极值预报的精度。
Description
本发明涉及径流预报技术领域，尤其是涉及一种基于基流分割和人工神经网络模型的径流预报方法。
径流预报是水资源管理的重要内容，也是一项具有挑战性的研究课题，一直是水文实践中最受关注的问题之一。径流预报能够为水资源管理提供有用的信息，同时对水资源的优化管理和有效利用具有重要意义，例如水力发电、旱涝灾害评估、水资源调度和供水配置等。然而，径流预报的预报提前期较长，期间预报的降水量具有内在的不确定性，因此预报面临重大挑战。
近年来，学者们研发了各种方法来提高中长期径流预报的精度，主要包括动物理模型、统计学模型和机器学习模型三大类。其中，物理模型应用降水量和其他气候变量的驱动，进而推动水文模型模拟日、月和年的水文过程。统计学模型以描述径流系列自身的统计关系，从而产生具有量化不确定性的径流预测，如自回归模型和时间序列分析模型。机器学习模型能够处理大量的数据和描述非线性关系而成为当下中长期径流预报的主流方法，如人工神经网络、支持向量机和随机森林等。尽管统计学模型和机器学习模型缺乏水文物理过程分析，数据驱动模型已被证明是非常简单并行之有效的径流预测方法。
人工神经网络是由大量的简单基本元件-神经元相互联结而成的自适应非线性动态系统。其中，Long Short-Term Memory(LSTM)是一种特殊的递归人工神经网络，由于其复杂的网络结构，能够很好地处理长期时间序列数据，并且该模型被广泛地用在中长期水文预报领域。虽然LSTM模型的预测准确性较好，但是其预测精度存在不稳定性，导致水文过程中的极值预测达不到较高标准，进而影响后续水资源配置工作。
发明内容
本发明的目的在于针对现有模型技术的不足，提供一种基于基流分割和人工神经网络模型的径流预报方法，在中长期径流预报中考虑基流分割问题，将径流分割成基流和地表径流两部分，并将这两部分作为人工神经网络模型的驱动因子 预测中长期径流流量，从而提高水文过程模拟和极值预报的精度。
为实现上述目标，本发明采用的技术方案如下：
一种基于基流分割和人工神经网络模型的径流预报方法，包含以下步骤：
(1)获取流域水文站点的实测日尺度径流序列。
(2)基于日径流数据运用数字滤波基流分割法，将其分解成基流和地表径流两部分，计算方法如下：
式中，Q
i为径流；Q
s(i)为地表径流；i为时间步长；α为衰退系数，其取值范围在0.9到0.95之间。
根据上述方程，再通过下式计算基流Q
b(i)：
Q
b(i)＝Q
i-Q
s(i)
(3)通过尺度转换，将日尺度时间序列转化为月尺度数据，并且利用三分之二数据来训练模型，剩下的三分之一测试模型的精度。根据人工神经网络模型理论，将月尺度基流和地表径流作为模型的输入因子，月径流作为模型输出值；
设置纳什效率系数(NSE)和百分比偏差(Bias％)为评价指标，通过评价指标来展现模型的拟合效果，其中NSE越接近1表示预测效果越好，Bias％越接近0表示预测效果越好。其公式如下所示：
式中，
为月径流观测值；
为模型预测值；
为观测值的平均值；n为数据总数。
根据国家2008年发布的《水文情报预报规范》中预报项目精度等级划分指标，对一种基于基流分割和人工神经网络模型的中长期径流预报方法进行预报精度判定，如表1所示：
表1预报项目精度等级划分表
| 精度等级 | 甲 | 乙 | 丙 |
| 纳什效率系数 | NSE>0.90 | 0.90≥NSE≥0.70 | 0.70>NSE≥0.50 |
| 百分比偏差 | Bias％≤15 | 15<Bias％≤30 | 30<Bias％≤40 |
本发明步骤(2)和(3)中采用的基流和地表径流作为人工神经网络模型的预报因子来模拟径流的思想，旨在提高中长期预报水文过程拟合度，提升年最高和最低流量的预报精度。
通过采用上述手段，本发明的有益效果为：
(1)本发明针对径流序列的自相关性，提出基于数字滤波基流分割方法和人工神经网络模型的中长期径流预测模型，从而提高径流预报的精度和可靠度。
(2)本发明基于基流分割的思想，弥补了单一预报因子人工神经网络模型对年峰谷值模拟的不足，大大提升了模拟精度。
(3)本发明提出新的中长期预报方法，通过比对国家预报项目精度划分指标，能够达到最高标准，可以为流域水资源配置方案提供科学的理论支撑。
图1为本发明的一个流程示意图；
图2为本发明的一个具体实例的数字滤波法基流分割结果图；
图3为本发明的一个具体实例的实测—模拟流量过程线；
图4为本发明的一个具体实例的实测—模拟年最大流量散点图；
图5为本发明的一个具体实例的实测—模拟年最小流量散点图；
下面通过实例，并结合附图，对本发明的技术方案做进一步详细说明。为了突出本发明的优势，运用基流和地表径流为输入的人工神经网络模型模拟某一流域水文站点的水文过程线，同时与单一预报因子(径流序列)的人工神经网络模型进行模拟效果比对。
如图1所示，本发明的一种基于基流分割和人工神经网络模型的中长期径流预报方法，包括如下步骤：
(1)数据收集和处理：收集水文站点1961-2000年的实测流量序列，通过数字滤波法对径流数据进行基流分割，其计算公式如下：
式中，Q
i为径流；Q
s(i)为地表径流；i为时间步长；α为衰退系数，其取值范 围在0.9到0.95之间，本方法取值为0.925。
根据上述方程，再通过下式计算基流(Q
b(i))：
Q
b(i)＝Q
i-Q
s(i)
为了修正相位的失真，该方法普遍采用正-反-正三次滤波得到水文站点的基流和地表径流序列，这样操作可以使基流曲线更加平滑，如图2所示。
(2)数据整合和分割：将步骤(1)计算所得的日尺度水文站点径流、基流和地表径流流量整合成月尺度序列数据。将这三组水文数据分割为训练期和测试期，训练期取整体数据的70％，余下的30％为测试期。其中，取1961-1988年为训练期，1989-2000年为测试期。
(3)模型输入和输出：选取LSTM人工神经网络模型作为本发明的示例模型。根据LSTM预报模型理论，将单一预报因子(径流)和两个预报因子(基流和地表径流)分别作为模型输入，模型输出均为月径流数据。图3为水文站点的实测径流和两个方案的模拟流量过程线。从图中结果来看，基于基流分割和人工神经网络模型的中长期径流预报方法得到的流量过程线比单一预报因子的人工神经网络预报模型模拟结果拟合效果更好。
模型整体模拟效果验证：将步骤(3)所得的模拟结果，代入评价指标公式，从数值结果上体现模型的拟合效果，具体计算方程式如下：
式中，
为月径流观测值；
为模型预测值；
为观测值的平均值；n为数据总数。
本发明方法和单一预报输入LSTM模型预报结果分析如表2所示：
表2模型模拟效果分析
本发明方法相对单一预报因子LSTM模型的模拟结果有较大的提升，测试期中NSE数值从0.782提高到0.904，并且Bias％的结果也有较大的提高(-0.353％到0.195％)。根据我国《水文情报预报规范》规定，确定性系数大于0.90为精度甲等级，本模型验证期纳什效率为0.904，与此同时，百分比偏差符合小于15％的指标，说明本预报模型的效果很好，可用于该水文站点的中长期径流预报以及后续的水资源优化配置。
模型峰谷值拟合效果验证：图4和5分别为水文站点实测与模型模拟年最小和最大流量散点图。通过图中结果所示，基于基流分割和LSTM模型的预报方法对年最大和最小月径流值拟合效果更好，相比于单一预报因子LSTM模型预报结果有很大程度上的提高。
以上所述仅对本发明的实例实施而已，并不用于限制本发明，发明中的预报因子(基流和地表径流)可以根据不同的研究区域的径流序列采用数字滤波法进行基流分割，同时人工神经网络模型也可以根据不同的研究区域进行模型训练和核函数选择。凡是在本发明的权利要求限定范围内，所做的任何修改、等同替换、改进等，均应在本发明的保护范围之内。
Claims (3)
- 一种基于基流分割和人工神经网络模型的径流预报方法，其特征在于，包含以下步骤：获取流域水文站点的实测径流序列，基于日径流数据运用数字滤波基流分割法，将其分解成基流和地表径流两部分，地表径流如下：
式中，Q i为径流；Q s(i)为地表径流；i为时间步长；α为衰退系数根据上述方程，再计算基流Q b(i)：Q b(i)＝Q i-Q s(i)将月尺度下的基流和地表径流作为人工神经网络模型的输入因子，将月径流作为模型的输出值，进行训练和预测。 - 根据权利要求1所述的基于基流分割和人工神经网络模型的径流预报方法，其特征在于，其中α取值范围在0.9到0.95之间。
- 根据权利要求1所述的基于基流分割和人工神经网络模型的径流预报方法，其特征在于，采用纳什效率系数NSE和百分比偏差Bias％作为模型评价指标。
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|---|---|---|---|---|
| CN115374638A (zh) * | 2022-08-24 | 2022-11-22 | 中国水利水电科学研究院 | 一种基于径流模拟的城市不透水面空间管控方法 |
| CN116539006A (zh) * | 2023-05-19 | 2023-08-04 | 清华大学 | 径流预测方法、装置、计算机设备和存储介质 |
| CN117252312A (zh) * | 2023-11-16 | 2023-12-19 | 水利部水利水电规划设计总院 | 考虑预报不确定性的区域水网系统水资源联合优化调度方法和系统 |
| CN117851757A (zh) * | 2024-01-11 | 2024-04-09 | 广东工业大学 | 基于机器学习的河流流量插补方法及装置、电子设备 |
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| CN113807545A (zh) * | 2021-09-09 | 2021-12-17 | 浙江大学 | 一种基于深度学习和物理模型的河湖生态流量预报预警方法 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108018823A (zh) * | 2017-12-15 | 2018-05-11 | 河海大学 | 基于瞬时单位线的流域地下水平均响应时间估计方法 |
| US20190227194A1 (en) * | 2015-12-15 | 2019-07-25 | Wuhan University | System and method for forecasting floods |
| CN111260111A (zh) * | 2020-01-08 | 2020-06-09 | 中国科学院上海技术物理研究所苏州研究院 | 基于气象大数据的径流预报改进方法 |
| CN111311026A (zh) * | 2020-03-19 | 2020-06-19 | 中国地质大学（武汉） | 一种顾及数据特征、模型和校正的径流非线性预测方法 |
| CN112330065A (zh) * | 2020-11-26 | 2021-02-05 | 浙江大学 | 一种基于基流分割和人工神经网络模型的径流预报方法 |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314554B (zh) * | 2011-08-08 | 2013-12-25 | 大唐软件技术股份有限公司 | 一种基于陆气耦合的小流域洪水预报方法及系统 |
| CN107274031A (zh) * | 2017-06-29 | 2017-10-20 | 华中科技大学 | 一种耦合神经网络和分布式vic模型的水文预报方法及系统 |
-
2020
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190227194A1 (en) * | 2015-12-15 | 2019-07-25 | Wuhan University | System and method for forecasting floods |
| CN108018823A (zh) * | 2017-12-15 | 2018-05-11 | 河海大学 | 基于瞬时单位线的流域地下水平均响应时间估计方法 |
| CN111260111A (zh) * | 2020-01-08 | 2020-06-09 | 中国科学院上海技术物理研究所苏州研究院 | 基于气象大数据的径流预报改进方法 |
| CN111311026A (zh) * | 2020-03-19 | 2020-06-19 | 中国地质大学（武汉） | 一种顾及数据特征、模型和校正的径流非线性预测方法 |
| CN112330065A (zh) * | 2020-11-26 | 2021-02-05 | 浙江大学 | 一种基于基流分割和人工神经网络模型的径流预报方法 |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115374638A (zh) * | 2022-08-24 | 2022-11-22 | 中国水利水电科学研究院 | 一种基于径流模拟的城市不透水面空间管控方法 |
| CN116539006A (zh) * | 2023-05-19 | 2023-08-04 | 清华大学 | 径流预测方法、装置、计算机设备和存储介质 |
| CN116539006B (zh) * | 2023-05-19 | 2023-10-03 | 清华大学 | 径流预测方法、装置、计算机设备和存储介质 |
| CN117252312A (zh) * | 2023-11-16 | 2023-12-19 | 水利部水利水电规划设计总院 | 考虑预报不确定性的区域水网系统水资源联合优化调度方法和系统 |
| CN117252312B (zh) * | 2023-11-16 | 2024-01-23 | 水利部水利水电规划设计总院 | 考虑预报不确定性的区域水网水资源联合优化调度方法 |
| CN117851757A (zh) * | 2024-01-11 | 2024-04-09 | 广东工业大学 | 基于机器学习的河流流量插补方法及装置、电子设备 |
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