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Research on runoff variations based on wavelet analysis and wavelet neural network model: A case study of the Heihe River drainage basin (1944-2005) 被引量:6
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作者 WANG Jun MENG Jijun 《Journal of Geographical Sciences》 SCIE CSCD 2007年第3期327-338,共12页
The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in Chin... The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge (1944-2005) and the Zhengyi Gorge (1954-2005), which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that: (1) annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; (2) annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; (3) prediction results show that: during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; (4) the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin. 展开更多
关键词 annual runoff variations wavelet analysis wavelet neural network model GIS spatial analysis HeiheRiver drainage basin
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Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction 被引量:10
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作者 Chuan GAO Xinrong WU Rong-Hua ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2016年第7期875-888,共14页
A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the ... A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The "observation" of the SST anomaly, which is sampled from a "truth" model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction. 展开更多
关键词 Four-dimensional variational data assimilation intermediate coupled model twin experiment ENSO prediction
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Diurnal variation models for fine fuel moisture content in boreal forests in China 被引量:3
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作者 Ran Zhang Haiqing Hu +1 位作者 Zhilin Qu Tongxin Hu 《Journal of Forestry Research》 SCIE CAS CSCD 2021年第3期1177-1187,共11页
Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a bo... Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China. 展开更多
关键词 Forest fuel Forest fire Moisture content prediction model Diurnal variation
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Settlement Prediction of Dredger Fill with the Optimal Combination Model 被引量:2
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作者 王清 闫欢 +2 位作者 苑晓青 牛岑岑 张旭东 《Journal of Donghua University(English Edition)》 EI CAS 2014年第6期812-816,共5页
Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. ... Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field. 展开更多
关键词 dredger fill settlement prediction combination model coefficient of variation WEIGHT
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Application of hydrological models in a snowmelt region of Aksu River Basin 被引量:1
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作者 Ouyang Rulin Ren Liliang +1 位作者 Cheng Weiming Yu Zhongbo 《Water Science and Engineering》 EI CAS 2008年第4期1-13,共13页
This study simulated and predicted the runoff of the Aksu River Basin, a typical river basin supplied by snowmelt in an arid mountain region, with a limited data set and few hydrological and meteorological stations. T... This study simulated and predicted the runoff of the Aksu River Basin, a typical river basin supplied by snowmelt in an arid mountain region, with a limited data set and few hydrological and meteorological stations. Two hydrological models, the snowmelt-runoff model (SRM) and the Danish NedbФr-AfstrФmnings rainfall-runoff model (NAM), were used to simulate daily discharge processes in the Aksu River Basin. This study used the snow-covered area from MODIS remote sensing data as the SRM input. With the help of ArcGIS software, this study successfully derived the digital drainage network and elevation zones of the basin from digital elevation data. The simulation results showed that the SRM based on MODIS data was more accurate than NAM. This demonstrates that the application of remote sensing data to hydrological snowmelt models is a feasible and effective approach to runoff simulation and prediction in arid unguaged basins where snowmelt is a major runoff factor. 展开更多
关键词 hydrological model snowmelt-runoff model (SRM) Danish NedbФr-AfstrФmnings model (NAM) remote sensing runoff simulation and prediction snowmelt region unguaged basin Aksu River Basin
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A BOUNDARY NESTING SCHEME OF LIMITED AREA MODEL NESTED WITH GLOBAL MODEL FOR FORECASTING TROPICAL CYCLONE OVER SOUTH CHINA SEA
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作者 万齐林 王康玲 《Journal of Tropical Meteorology》 SCIE 1998年第1期1-7,共7页
Today, the nested model is used widely. The effect and role of each nested variate is analyzed in this paper. It is found that the forecast could benefit by nesting part of the variates and the effect of each of the v... Today, the nested model is used widely. The effect and role of each nested variate is analyzed in this paper. It is found that the forecast could benefit by nesting part of the variates and the effect of each of the variates may be different. Therefore, only the effectual variates are chosen for the nesting. According to this finding, a scheme is suggested and applied to the limited area mode (TL10) nested with global spectral mode (T63) for forecasting tropical cyclones over the South China Sea. A few numerical prediction tests show that this scheme is reasonable and efficient. 展开更多
关键词 nested model effect of nested variates NUMERICAL prediction test
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Surface Soil Moisture Simulation for a Typical Torrential Event with a Modified Noah LSM Coupling to the NWP Model
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作者 ZHENG Zi-Yan ZHANG Wan-Chang +2 位作者 XU Jing-Wen YAN Zhong-Wei LU Xue-Mei 《Atmospheric and Oceanic Science Letters》 2011年第1期18-23,共6页
Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance o... Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist. 展开更多
关键词 soil moisture Noah LSM hydrologic runoff parameterization Numerical Weather prediction (NWP) model
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TOPMODEL模型在新疆伊犁河流域水文计算中的应用研究 被引量:2
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作者 朱文静 《地下水》 2021年第2期153-156,共4页
水文模型对于分析水文活动具有重要的参考意义。TOPMODEL模型是水文模型中应用最广泛的模型之一,本文对TOPMODEL模型的基本理论、基本方程、模型参数以及应用步骤进行阐述,并基于TOPMODEL模型对伊犁河流域进行径流模拟、洪水预报及水文... 水文模型对于分析水文活动具有重要的参考意义。TOPMODEL模型是水文模型中应用最广泛的模型之一,本文对TOPMODEL模型的基本理论、基本方程、模型参数以及应用步骤进行阐述,并基于TOPMODEL模型对伊犁河流域进行径流模拟、洪水预报及水文计算模拟研究,模拟结果显示:TOPMODEL模型能够很好的再现伊犁河流域的洪水径流过程,有8个场的测定系数达到丙级以上精度,洪峰相对误差小于20%,合格率为88.9%,研究结果能够为伊犁河流域及相似流域水文活动的预报预警提供一定的理论和技术参考。 展开更多
关键词 TOPmodel模型 水文计算 径流模拟 洪水预报 伊犁河流域
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Prediction for permeability index of blast furnace based on VMD-PSO-BP model 被引量:1
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作者 Xiao-jie Liu Yu-jie Zhang +4 位作者 Xin Li Zhi-feng Zhang Hong-yang Li Ran Liu Shu-jun Chen 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2024年第3期573-583,共11页
The permeability index is one of the important production indicators to monitor the operation of blast furnace.It is crucial to grasp the trends of changes in the new permeability index in time.For the complex vibrati... The permeability index is one of the important production indicators to monitor the operation of blast furnace.It is crucial to grasp the trends of changes in the new permeability index in time.For the complex vibration spectrum of the permeability index,a prediction model of the permeability index based on the VMD-PSO-BP(variational mode decomposition-particle swarm optimization-back propagation)method was proposed.Firstly,the key factors that affect the permeability index of blast furnace were studied from multiple perspectives.Then,the permeability index was divided into multiple sub-modes based on the difference of frequency bands by the VMD algorithm,and a PSO-BP prediction model was established for each sub-mode.Finally,the prediction results of each sub-mode were summed to obtain the final one.The results show that the composite prediction accuracy by using the VMD algorithm is 3%higher than that of the traditional prediction method,which has better applicability. 展开更多
关键词 Big data-Blast furnace Air permeability variational mode decomposition Particle swarm optimization Back propagation model prediction
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A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions 被引量:4
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作者 Cheng Fan Meiling Chen +1 位作者 Rui Tang Jiayuan Wang 《Building Simulation》 SCIE EI CSCD 2022年第2期197-211,共15页
Short-term building energy predictions serve as one of the fundamental tasks in building operation management.While large numbers of studies have explored the value of various supervised machine learning techniques in... Short-term building energy predictions serve as one of the fundamental tasks in building operation management.While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven models.One promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive modeling.This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions.Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively.Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings.The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy predictions.The average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data.The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management. 展开更多
关键词 building energy predictions data augmentation data-driven models generative modeling variational autoencoders
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基于多模型融合的中长期径流集成预测方法 被引量:1
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作者 朱非林 陈嘉乙 +2 位作者 张咪 徐向荣 钟平安 《水力发电》 CAS 2024年第2期6-13,29,共9页
中长期水文预报是流域水资源规划与合理配置的重要依据。为提高中长期径流预测精度,提出了一种基于多模型融合的水库中长期径流集成预测方法。该方法将ARMA、BP、LSTM、RF和SVR等5个异质预测模型进行融合,同时采用超参数优化方法确定各... 中长期水文预报是流域水资源规划与合理配置的重要依据。为提高中长期径流预测精度,提出了一种基于多模型融合的水库中长期径流集成预测方法。该方法将ARMA、BP、LSTM、RF和SVR等5个异质预测模型进行融合,同时采用超参数优化方法确定各模型的最优参数。将其用于青海省龙羊峡水库的中长期径流预报中,结果表明,通过Stacking融合算法建立的集成预测模型相较于单一模型,取得了更高的预测精度(R2值由0.71提升至0.82)。此方法可为提升流域中长期径流预测精度提供一定参考。 展开更多
关键词 中长期径流预报 ARMA BP LSTM RF SVR 多模型融合 集成预测 Stacking融合算法 超参数寻优 龙羊峡水库
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径流序列相空间重构的水文学含义及应用
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作者 李建林 贺奇 +2 位作者 王树威 王心义 张杰 《水资源保护》 EI CAS CSCD 北大核心 2024年第3期90-97,148,共9页
为确定径流序列相空间重构后的水文学含义并提高径流中长期预测精度,基于混沌理论进行径流序列相空间重构,并对径流影响因素与重构后相空间列向量进行相关性分析。在此基础上建立了混沌理论与人工神经网络耦合(Chaos-BPNN)的径流预测模... 为确定径流序列相空间重构后的水文学含义并提高径流中长期预测精度,基于混沌理论进行径流序列相空间重构,并对径流影响因素与重构后相空间列向量进行相关性分析。在此基础上建立了混沌理论与人工神经网络耦合(Chaos-BPNN)的径流预测模型,并应用于黑河上游莺落峡水文站和正义峡水文站。结果表明:径流序列重构后相空间列向量具有明确的水文学含义;Chaos-BPNN径流预测模型仅需径流序列数据就可进行建模和预测,规避了径流预测过程中主控因素难以确定和不易量化的问题;黑河上游降水量、输沙量、水位和气温分别与重构后相空间的第1、3、6、7列具有较高的相关性,风速与任何一列都不相关,推测雪线高程、植被覆盖率以及土地利用类型等因素与第2、4、5列存在相关性;构建的Chaos-BPNN径流预测模型在黑河上游莺落峡水文站和正义峡水文站的径流预测精度均在86%以上。 展开更多
关键词 径流序列 相空间重构 混沌特征 径流影响因素 Chaos-BPNN径流预测模型
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基于改进金豺算法的短期负荷预测 被引量:2
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作者 谢国民 王润良 《电力系统及其自动化学报》 CSCD 北大核心 2024年第3期65-74,共10页
针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用... 针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用改进金豺算法对双向长短期记忆网络的参数进行优化,并对每个子序列建立预测模型;最后,组合各模型结果得到最终预测值。实验结果表明,本文模型预测精度更高,与真实值拟合度更好。 展开更多
关键词 变分模态分解 改进金豺算法 双向长短期记忆 组合模型 短期负荷预测
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基于VMD-TCN-GRU模型的水质预测研究 被引量:1
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作者 项新建 许宏辉 +4 位作者 谢建立 丁祎 胡海斌 郑永平 杨斌 《人民黄河》 CAS 北大核心 2024年第3期92-97,共6页
为充分挖掘水质数据在短时震荡中的变化特征,提升预测模型的精度,提出一种基于VMD(变分模态分解)、TCN(卷积时间神经网络)及GRU(门控循环单元)组成的混合水质预测模型,采用VMD-TCN-GRU模型对汾河水库出水口高锰酸盐指数进行预测,并与此... 为充分挖掘水质数据在短时震荡中的变化特征,提升预测模型的精度,提出一种基于VMD(变分模态分解)、TCN(卷积时间神经网络)及GRU(门控循环单元)组成的混合水质预测模型,采用VMD-TCN-GRU模型对汾河水库出水口高锰酸盐指数进行预测,并与此类研究中常见的SVR(支持向量回归)、LSTM(长短期记忆神经网络)、TCN和CNN-LSTM(卷积神经网络-长短期记忆神经网络)这4种模型预测结果对比表明:VMD-TCN-GRU模型能更好挖掘水质数据在短时震荡过程中的特征信息,提升水质预测精度;VMD-TCN-GRU模型的MAE(平均绝对误差)、RMSE(均方根误差)下降,R^(2)(确定系数)提高,其MAE、RMSE、R^(2)分别为0.0553、0.0717、0.9351;其预测性能优越,预测精度更高且拥有更强的泛化能力,可以应用于汾河水质预测。 展开更多
关键词 水质预测 混合模型 变分模态分解 卷积时间神经网络 门控循环单元 时间序列 汾河
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考虑多时间尺度信息的风力发电机滚动轴承故障预测
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作者 赵洪山 林诗雨 +2 位作者 孙承妍 杨伟新 张扬帆 《中国电机工程学报》 EI CSCD 北大核心 2024年第22期8908-8919,I0018,共13页
风电机组滚动轴承故障会造成风电机组长时间停机,为准确预测风电机组滚动轴承故障,提出一种考虑多时间尺度信息的风力发电机滚动轴承故障预测方法。首先,采用连续变分模式分解(successive variational mode decomposition,SVMD)自适应... 风电机组滚动轴承故障会造成风电机组长时间停机,为准确预测风电机组滚动轴承故障,提出一种考虑多时间尺度信息的风力发电机滚动轴承故障预测方法。首先,采用连续变分模式分解(successive variational mode decomposition,SVMD)自适应提取轴承健康数据温度多维特征;其次,将分解的本征模态函数(intrinsic mode functions,IMFs)输入Informer模型提取多尺度时间信息训练,基于树状结构Parzen密度估计的非标准贝叶斯优化算法(tree structure Parzen density estimation,TPE)优化Informer模型超参数;然后,构建基于残差的故障指标,采用核密度估计(kernel density estimation,KDE)确定故障预警阈值;最后,将运行数据输入训练后的Informer模型进行故障预测。选取某风电场的风力发电机轴承温度数据进行故障预测,仿真结果表明,考虑多时间尺度信息的SVMD-TPE-Informer模型在发电机轴承温度预测上具有更高的预测精度和计算效率,所提方法在两个故障案例上分别能够提前15.5 h和10 h预测到故障,且不会出现误报现象,验证所提模型的有效性和稳定性。 展开更多
关键词 连续变分模式分解 贝叶斯优化 Informer模型 故障预测
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宽度-深度融合时频分析的径流智能预测方法
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作者 韩莹 王乐豪 +2 位作者 王淑梅 张翔 罗星星 《系统仿真学报》 CAS CSCD 北大核心 2024年第2期363-372,共10页
为解决现有基于LSTM的径流预测模型易陷入局部最优的问题,提出了基于VMD-LSTMBLS(variational mode decomposition-LSTM-broad learning system)的径流预测模型。将宽度学习系统与LSTM结合,针对径流序列多噪音特点,采用时频分析方法中... 为解决现有基于LSTM的径流预测模型易陷入局部最优的问题,提出了基于VMD-LSTMBLS(variational mode decomposition-LSTM-broad learning system)的径流预测模型。将宽度学习系统与LSTM结合,针对径流序列多噪音特点,采用时频分析方法中的变分模态分解,将径流时间序列的一维时域信号变换到二维时频平面,减少噪声对预测结果的影响。仿真结果表明:与基线模型及现有基于LSTM的径流预测模型相比,该模型的预测精度有较为明显的提高。 展开更多
关键词 径流预测 变分模态分解 长短时记忆网络 宽度学习系统 时频分析 智能预测
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基于AVMD多尺度模糊熵和VPMCD算法的宽频振荡分类
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作者 赵妍 潘怡 +1 位作者 李亚波 聂永辉 《电力系统保护与控制》 EI CSCD 北大核心 2024年第13期179-187,共9页
电力系统宽频振荡具有宽频域、非线性和时变性的特点,对振荡分类在准确性、快速性等方面提出了更高的要求。为此,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的多尺度模糊熵(multi-scale fuzzy entr... 电力系统宽频振荡具有宽频域、非线性和时变性的特点,对振荡分类在准确性、快速性等方面提出了更高的要求。为此,提出一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的多尺度模糊熵(multi-scale fuzzy entropy,MFE)和变量预测模型(variable predictive model-based class discriminate,VPMCD)相结合的宽频振荡分类新方法。首先,对宽频振荡信号进行AVMD,得到固有模态分量(intrinsic mode functions,IMFS)。然后,引入MFE对IMFS进行时域特征描述,同时实现对IMFS构造特征向量的降维处理。最后,采用VPMCD对MFE降维后的特征向量实现宽频振荡的分类检测。通过仿真和实测数据分析,结果表明,所提方法的宽频振荡分类检测准确率比支持向量机(support vector machines,SVM)、BP神经网络方法的分类准确率更高,分类时间更短。 展开更多
关键词 宽频振荡分类 多尺度模糊熵 变分模态分解 变量预测模型
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“分解-校正-集成”模式下基于深度信念网络模型的径流预测 被引量:1
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作者 钱玉霞 陈伏龙 +3 位作者 何朝飞 龙爱华 孙怀卫 吕廷波 《长江科学院院报》 CSCD 北大核心 2024年第5期35-44,共10页
精准的短期径流预测可为流域内水资源规划、防洪调度及抗旱减灾工作提供重要的科学依据。为减小模型的系统误差,提高径流预测精度,在“分解-集成”模式的基础上提出“分解-校正-集成”框架,构建EEMD-DBN-EnKF、VMD-DBN-EnKF模型。利用... 精准的短期径流预测可为流域内水资源规划、防洪调度及抗旱减灾工作提供重要的科学依据。为减小模型的系统误差,提高径流预测精度,在“分解-集成”模式的基础上提出“分解-校正-集成”框架,构建EEMD-DBN-EnKF、VMD-DBN-EnKF模型。利用集合卡尔曼滤波数据同化算法对偏离实测径流过大的分量校正以降低分解子序列在预测中产生的系统误差,并与未修正的EEMD-DBN、VMD-DBN模型及单一DBN模型进行了对比分析。结果表明:基于模态分解的组合模型较单一模型RMSE减小了至少23%,NSE与R^(2)增加了21%以上;基于径流分量校正的组合模型相较于模态分解的组合模型各评价系数有所提升,其中VMD-DBN-EnKF预测模型误差最小,效果最优,NSE与R^(2)达到0.89以上,其次依次为EEMD-DBN-EnKF>VMD-DBN>EEMD-DBN。综上“分解-校正-集成”模式的预测框架在玛纳斯河流域具有良好的适用性,可为玛纳斯河径流短期预报提供技术支持。 展开更多
关键词 模态分解 深度信念网络 集合卡尔曼滤波 径流预测 组合模型
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考虑记忆时间的LSTM模型在赣江流域径流预报中的应用 被引量:1
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作者 胡乐怡 蒋晓蕾 +4 位作者 周嘉慧 欧阳芬 戴逸姝 章丽萍 付晓雷 《湖泊科学》 EI CAS CSCD 北大核心 2024年第4期1241-1251,I0030,共12页
在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景... 在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景分析:模型的有效预见期与不同流域平均产汇流时间之间的关系,有效预见期内LSTM径流预测模型精度与记忆时间之间的关系,不同长度的预见期与模型最佳记忆时间之间的关系,同时探讨LSTM径流预测所需的记忆时间与流域面积的关系。结果表明:(1)综合考虑降水和前期径流情景下的径流预测效果最好,当预见期为1 d时,外洲、峡江、栋背站的纳什效率系数(NSE)分别可达0.98、0.96以及0.90;且其有效预见期与仅考虑降水信息的有效预见期相同,均与流域平均产汇流时间相近。(2)随着预见期的延长,不同情景下的预测精度均有不同程度的下降,其中仅考虑前期径流情景的下降率最大,说明降水信息较前期径流对径流预测效果的提升更重要。同时,随着流域面积的增加,相同预见期内径流预测精度均有所提升。(3)当预见期相同时,随记忆时间的延长,不同径流预测模型的预测精度均先上升至最高,接着具有下降趋势,最后逐渐趋于稳定。且在有效预见期内,随着预见期的延长,最佳记忆时间均有增大趋势,当达到最长的有效预见期时,对应的最佳记忆时间均为14 d。此外,在赣江流域的模拟结果表明,随着流域面积的增大,LSTM的最佳记忆时间减小。研究结果可为赣江流域的径流预报提供参考,同时有助于推求其他流域采用机器学习进行径流预测所需的最佳记忆时间。 展开更多
关键词 LSTM模型 赣江流域 记忆时间 径流预测 预见期
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基于VMD与组合模型的大气污染物浓度预测方法
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作者 邵玉祥 冯春生 +2 位作者 程俊杰 刘秋梦 蒲思涵 《软件导刊》 2024年第4期8-13,共6页
为提高大气污染物浓度的预测准确性,提出一种基于变分模态分解与组合模型的预测方法。首先通过变分模态分解将目标监测点的历史污染物浓度数据重构为多变量时序数据,根据区域内监测点之间的地理关系构建时空序列数据;然后将处理好的数... 为提高大气污染物浓度的预测准确性,提出一种基于变分模态分解与组合模型的预测方法。首先通过变分模态分解将目标监测点的历史污染物浓度数据重构为多变量时序数据,根据区域内监测点之间的地理关系构建时空序列数据;然后将处理好的数据输入LSTM与ConvLSTM的组合模型中,同时提取时间与空间特征并输出预测结果。针对武汉市PM2.5、SO2、NO23种污染物历史浓度数据进行实验,所提预测方法在MAE、RMSE和MAPE3个指标上均表现最佳,明显优于其他模型。此外,在时间尺度增加的情况下,该方法相较其他模型仍保持最高的预测精度。该方法能够充分捕捉局部特征,在综合考虑时间与空间特征方面具备显著优势,为大气污染物浓度的准确预测提供了一种可行途径。 展开更多
关键词 大气污染物 浓度预测 变分模态分解 组合模型 LSTM ConvLSTM
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