基于互信息与神经网络的天山西部山区融雪径流中长期水文预报

为提高天山西部山区融雪径流的预报精度,更好地指导所在区域的工农业生产发展,针对影响预报精度的关键问题(预报因子的选择),基于互信息法、相关系数法、主成分分析法对研究区的预报因子进行优选,采用RBF神经网络以及组合小波BP神经网络模型进行径流预报研究,并进行不同方案的比较。结果表明:①互信息法优选出的预报因子作为模型输入可以提高预报精度;②采用不同优选预报因子作为RBF神经网络以及组合小波BP神经网络模型的输入变量,结果表明RBF神经网络模型的预测精度要好于组合小波BP神经网络模型;③以相对误差作为评价模型精确度的标准,预测效果最好的是基于互信息方法挑选出的预报因子作为RBF神经模型输入数据的模型预测结果。     

基于神经网络与遗传算法的洪水分类预报研究

针对水文模型参数的不确定性,对洪水进行分类预报,不同类型洪水采用不同预报参数,旨在提高洪水预报精度。基于BP神经网络模型,依据分类因子选取原则,选取6项具有代表性的影响因子作为模型输入,可将洪水划分成高、中、低3类。基于遗传算法,对3类洪水进行参数率定,获得3组不同的参数组,最终利用训练好的分类预报模型实现不同类型洪水的变参数预报。以大伙房水库25场历史典型洪水进行实例验证与分析,结果表明:分类预报结果的洪峰误差、峰现误差、确定性系数及典型洪水过程的拟合效果明显优于分类前。经训练后的基于BP神经网络与遗传算法的洪水分类预报模型可较好适用于大伙房水库,结果更贴合实测值,效果整体上优于分类前,方法可行、有效。     

基于长短时记忆神经网络（LSTM）的降雨径流模拟及预报

长短时记忆神经网络(LSTM)具有很强的时间序列关系拟合能力,非常适用于模拟及预报流域产汇流这一复杂的时间序列过程。基于LSTM针对不同预见期的径流预报分别建立了流域降雨径流模型,以探讨LSTM在水文预报当中的应用。模型采用流域降雨、气象及水文数据作为输入,不同预见期后的径流过程作为输出,率定期为14a,验证期为2a。结果显示,在预见期为0～2d时LSTM预报精度很高,在预见期为3d时预报精度较差,但仍优于新安江模型。隐藏层神经元数量作为神经网络复杂程度的代表既会影响模型预报精度,也会影响模型训练速度。而输入数据长度则仅会在预见期为0的条件下影响模型预报效果。     

基于BP-神经网络的风速短期预报技术研究

针对风速预报中出现的资料获取困难、预报精度差等问题,文章提出采用临近历史数据的BP-神经网络风速短期预报模型,并重点对BP模型的输入层和隐含层参数进行估计。在一定范围内,枚举输入层和隐含层的参数,并采用大量数据进行模拟,同时采用SSE和MAE两种指标对模拟结果进行评价,得到了适合于风速预报的多个不同参数BP模型。同时将多个BP模型用于预报,发现预报结果精度都比较高,表明不同参数的BP模型均可用于预报且BP模型存在异参同效性。     

基于改进BP神经网络的水分入渗参数预测模型

基于晋北盐碱地土壤水分原位入渗试验,建立了容量为150组的盐碱地Philip入渗模型参数样本,借助MATLAB软件,分别构建基于最值归一化法、联合归一化法的BP神经网络预测模型,其中模型的输入变量为土壤基本理化参数,输出变量为Philip入渗模型参数吸渗率S和稳渗率A,由两模型的预测结果发现,预测误差均小于6%,在建模误差允许范围之内,所建模型可靠;对比模型预报结果发现,联合归一化法处理过的输入数据更具代表性,且提高了网络收敛速度及预测精度。用实测资料对基于联合归一化法建立的模型进行精度检验,结果表明对入渗参数预测的相对误差均小于10%,模型预报精度较高,可满足实际应用的要求。     

不同数据处理策略对Chl-a浓度预测精度的影响

为了确定合理数据处理策略,提高基于神经网络的叶绿素a含量预测精度,采用7种数据处理方案和5种神经网络输入参数组合,研究了不同数据处理策略对叶绿素a含量预测精度的影响。结果表明:数据处理可有效提高基于神经网络的水体中叶绿素a含量预测精度,不同数据处理策略得到的主成分不同,对预测精度的提高程度不同;在输入参数数量相同情况下,以格拉布斯准则处理异值点,再采用局部多项式回归进行数据平滑所得的神经网络预测精度最高;4个输入参数情况下,预测精度在进行数据处理后最高可达到0.986,比采用原始数据提高23.25%。     

根据历史位移预报大坝变形的神经网络方法

根据东江大坝变形水平位移实测数据分别建立统计模型和神经网络模型，以历史位移值作为参数来进行预测、预报，与采用变形因子作参数不同。实践表明，根据历史位移值来预报大坝变形是可行的，在预报精度方面，神经网络模型比统计模型得到的结果稍优。     

ARIMA与ANN组合预测模型在中长期径流预报中的应用

基于时间序列预测模型及BP神经网络,提出了新的组合预测方法.该方法采用三层结构的BP神经网络来构造组合预测模型,运用时间序列模型预测方法得出的预测结果,采用历史滚动法将前5年的预测结果数据作为BP网络的输入,以当前年份的预测结果为网络期望输入,建立了ARIMA-ANN组合预报模型.利用Matlab7神经网络工具箱对塔里木河上游源流卡群水文站的年径流量进行了预报及验证.结果表明:组合模型的预报结果精度高,容错能力强,是中长期径流预报的有效方法.     

黄龙滩水库中长期径流预报方法研究

基于黄龙滩水库和潘口水库历史旬月径流数据,选取其2012年～2018年的径流、降雨数据进行灰色关联分析,筛选出与黄龙滩水库入库径流关联度最高的7个预报因子,建立深度神经网络(DNN)、Elman神经网络和支持向量机(SVM)径流预测模型,对模型参数进行训练,统计模型训练期和检验期的确定性系数、洪峰合格率、均方差和平均相对误差。预报效果表明,3种模型在黄龙滩水库中长期径流预测上效果较好,精度较高,误差较小,预报结果对于黄龙滩水库水文预报上具有重要意义。相比于深度神经网络和Elman神经网络,支持向量机在洪峰预报上误差更小,且具有更高的预测精度。     

BP与LSTM神经网络在福建小流域水文预报中的应用对比

LSTM(长短期记忆)神经网络作为一种具有记忆能力的循环神经网络,能够学习时间序列数据间的状态特征,特别适合用于流域降雨径流预报。利用福建省延寿溪流域渡里水文站逐时降雨数据和逐时流量数据,分别采用模块化建模方法构建BP神经网络和LSTM神经网络,并采用集合预报均值的形式以避免模型训练中的参数局部最优解问题,进行未来1～24 h的逐时流量滚动预报。对比2个神经网络模型预报结果表明,LSTM模型整体预报效果优于BP模型,在滚动预报过程中预报精度的衰减速度大大慢于BP模型,1～24 h逐时预报的Nash效率系数为0. 968～0. 740,能够满足短期洪水预报精度要求。     

River Flow Forecasting using Recurrent Neural Networks

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km² up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting.     

考虑预见期降水的三峡水库区间洪水预报模型研究

 选用长江三峡水库区间流域的历史雨洪资料与短期定量降水预报资料,编制三峡水库区间流域洪水预报模型,并将区间定量降水预报与区间洪水预报模型相耦合,研究了预见期降水对洪水预报的影响。提出了一个随机降水模型,随机生成 500组序列作为降水预报值输入到区间洪水预报模型,并以均值作为预报结果发布。方案比较结果表明,考虑预见期内的降水预报可提高三峡水库的洪水预报精度。     

Lake Level Prediction using Feed Forward and Recurrent Neural Networks

The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .

     

Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network

Developing water level forecasting models is essential in water resources management and flood prediction. Accurate water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including river flow. Improving the ANN computational approach could help produce accurate forecasting results. Most studies conducted to date have used a sigmoid function in a multi-layer perceptron neural network as the basis of the ANN; however, they have not considered the effect of sigmoid steepness on the forecasting results. In this study, the effectiveness of the steepness coefficient (SC) in the sigmoid function of an ANN model designed to test the accuracy of 1-day water level forecasts was investigated. The performance of data training and data validation were evaluated using the statistical index efficiency coefficient and root mean square error. The weight initialization was fixed at 0.5 in the ANN so that even comparisons could be made between models. Three hundred rounds of data training were conducted using five ANN architectures, six datasets and 10 steepness coefficients. The results showed that the optimal SC improved the forecasting accuracy of the ANN data training and data validation when compared with the standard SC. Importantly, the performance of ANN data training improved significantly with utilization of the optimal SC.     

AR模型在宝珠寺水库实时洪水预报校正中的应用

人类活动改变了流域下垫面,对洪水预报精度产生极大的影响,引进实时校正模型以提高洪水预报精度。根据宝珠寺水库的自然地理和水文气象特性,宝珠寺水库实时洪水预报采用新安江模型,实时校正模型采用时间序列AR模型。利用10年历史降雨径流资料,对新安江和实时校正模型的参数进行率定,并利用近年的2次洪水对模型进行检验,检验结果表明实时校正能明显地提高洪水预报的精度。     

Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation

With the rapid development of Artificial Intelligence (AI) technology, the Long Short-Term Memory (LSTM) network has been widely used for forecasting hydrological process. To evaluate the effect of training data amount on the performance of LSTM, the study proposed an experiment scheme. First, K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series of 130 years based on the observed data, and the Soil and Water Assessment Tool (SWAT) model is used to obtain the corresponding runoff series with the generated meteorological data series. Then, the 130 years of rainfall and runoff data is divided into two parts: the first 80 years of data for model training and the remaining 50 years of data for model verification. Finally, the LSTM models are developed and evaluated, with the first 5-year, 10-year, 20-year, 40-year and 80-year data series as training data respectively. The results obtained in Yalong River, Minjiang River and Jialing River show that increasing the training data amount can effectively reduce the over-fittings of LSTM network and improve the prediction accuracy and stability of LSTM network.

     

基于人工神经网络的洪水水位预报模型

本文利用人工神经网络技术，以确定性系数为目标函数，建立以上游和本站水位资料预报本站未来若干时段洪水水位的预报模型，以探讨神经网络技术在水文预报中的应用。研究成果为提高网络训练速度和预报可能产生的超历史洪水情况，给出了输入/输出层的数据规范化的处理方法。选择珠江三角洲河网地区水位站资料，对预报模型进行检验，结果表明在合理选择输入层单元数据和预见期的条件下，可以取得很好的预报成果。     

ARIMA模型在卫星钟差预报中的应用

为提高GPS精密单点定位的精度(PPP),需要有高精度的卫星钟差预报。针对卫星钟差预报在卫星导航定位中的重要作用,利用时间序列模型与灰色模型对卫星钟差进行1 d和120 d的预报。结果表明,基于时间序列预报模型的预测精度优于灰色模型,更适用于实际应用,Rb钟的精度和稳定性优于Cs钟。     

改进的GM(1,1)模型在城市需水量预测中的应用

基本GM(1,1)模型未充分利用新信息,且背景值构造不合理,对变化非平稳的数据序列预测精度较低,为此,本文采用重构背景值和等维递补原理对基本GM(1,1)模型进行改进,建立重构背景值的GM(1,1)等维递补模型,并运用改进模型预测北方某市需水量,结果表明,改进模型预测精度更高,为需水量的预测提供了一种新方法.     

混沌神经网络在地表水资源量预测中的应用

为了有效地揭示水资源系统复杂的非线性结构及变化规律,对具有混沌特性的水资源时间序列重构相空间,计算出相空间的饱和嵌入维数和最大Lyapunov指数,并以此为指导,提出一种适用于高精度逼近和泛化建模的混沌神经网络的学习算法,运用混沌方法构造训练样本及确定神经网络的网络结构,用神经网络拟合相空间相点演化的非线性关系,建立混沌神经网络预测模型。实例表明,该模型有较高的预报精度。