带偏差单元的递归神经网络及其运用

近年来人工神经网络（ANN）在水文预测和水文分析中的应用越来越多，其中BP网络的应用尤为广泛，不少学者提出了很多基于改进算法的BP网络。通过研究，从改进网络结构出发，首次建立了带偏差单元的递归神经网络，并将它用于径流预测。应用实例表明，该结构的网络模型，通过改进网络结构，能很好地克服原BP模型收敛速度慢，网络学习，记忆不稳定等缺点。拟合，预报精度较原BP模型均有较大提高。     

地下水文预测中BP网络的模型结构及算法探讨

本文探讨了人工神经网络中不同BP网络结构和算法在区域地下水文预测中的应用，实例比较了不同层次结构、学习速率、隐层单元数及不同算法等对收敛效果、模拟预报结果的影响。提出了一些BP模型的设计应用技术，即学习速率的取值范围与BP网络层数有一定关系，层数多，稳定区间较小，一般学习速率取值为0.01～0.1。快速BP算法从训练速度，收敛精度等方面均优于普通BP算法，可作为改进BP算法之一。在此基础上根据黄河河套灌区多年的水文、气象和地下水信息，对灌区多年的年地下水埋深变化进行了模拟，预测了河套灌区节水工程实施后未来灌区地下水位下降的趋势，为大型灌区节水工程改造与BP模型在区域地下水文中的应用提供了参考。     

基于遗传学习算法和BP算法的神经网络在矿坑涌水量计算中的应用

本文采用遗传学习算法和误差反向传播算法(BP)相结合的混合算法来训练前馈人工神经网络(BPN)，即先用遗传学习算法进行全局训练，再用BP算法进行精确训练，使网络收敛速度加快和避免局部极小。作为实例，本文将该方法运用于多维时序问题。根据山东省黑旺铁矿的矿坑充水条件建立了一个网络，以矿坑充水的各种控制因素相关资料作为样本，对网络进行训练并用训练好的网络预测矿坑涌水量。网络的训练速度及预测结果表明，该算法收敛速度较快，预测精度很高，为矿坑涌水量预报提供了一种新思路和新方法。     

数据挖掘技术在径流预报中的应用

介绍了基于数据挖掘技术的径流预报方法,建立了以人工神经网络(ANN)为挖掘工具的水文预报模型,利用反向传播(BP)模型进行挖掘工作,通过权系数修正提高收敛速度.结合工程项目进行了模型的实际预测,并与普通模型相比较.实测结果和分析说明,该模型有较好的预测精度,有实际应用价值.     

大坝变形预测的径向基函数网络模型

在大坝变形预测中,运用人工神经网络模型进行预测分析已较为广泛,目前使用最多的是BP网络模型,但由于存在计算量巨大,且易出现局部极小和收敛慢等缺点,为此建立了大坝变形预测的径向基函数神经网络模型,并与改进的BP网络模型进行比较.实例表明,径向基函数模型具有良好的泛化能力,克服了BP模型的局部极小和收敛慢等缺陷,在预测精度及训练速度方面显著优于BP模型,具有一定的推广价值.     

偏最小二乘法与神经网络耦合的大坝监测模型

针对人工神经网络在大坝变形监测模型应用中所出现的收敛慢和稳定性差等问题,提出了偏最小二乘法与人工神经网络耦合的大坝变形监测模型,提高了神经网络的学习速率和稳定性.首先运用偏最小二乘法对多维自变量进行主成分提取和降维处理,解决了变量之间的多重相关问题,而后把降维的数据输入神经网络进行训练.对比实例应用结果表明,偏最小二乘神经网络耦合模型的拟合速度和精度都高于传统的神经网络.     

基于粒子群收缩因子算法的BP网络径流预测模型

BP网络模型在径流预测中应用较广,效果较好.但目前对BP网络的初始权重及偏值、学习率、动量因子和训练次数多采用"试错法"来确定,具有较大的不确定性,影响到模型的收敛速度和精度.为此,提出一种利用粒子群收缩因子算法(CFPSO)对BP模型上述参数进行优化的方法,并利用径流预测实例进行检验,计算结果表明该优化方法能够提高BP模型的收敛速度和精度.     

水质综合评价的B—P人工神经网络模型

为探索人工神经网络用于水质综合评价的可能性，提出了基于Ｂ－Ｐ算法的人工神经网络综合评价模型，实例研究表明，Ｂ－Ｐ人工神经网络用于水质综合评价简便实用，具有客观性和通用性。     

GRNN神经网络在坝基渗流预测中的应用

人工神经网络在大坝监测资料分析及预测中应用效果良好,而广义回归神经网络具有柔性网络结构、很强的非线性映射能力及高度的容错性,非常适合解决非线性问题。实例分析结果表明:与BP神经网络相比,广义回归神经网络在预测能力及学习速度上具有明显优势,且样本较少时其预测效果也较好。     

自适应遗传算法优化管网状态估计神经网络模型

针对遗传算法收敛速度慢、传统BP算法易收敛于局部最优以及网络结构难以确定等问题，引进自适应遗传算法优化网络的权阈值以搜寻网络最优拓扑结构，并利用自适应遗传算法优化该网络的权阈值，建立基于改进BP网络的宏观管网状态模型．实例分析表明，改进模型具有较高的预测精度。     

基于改进BP神经网络模型的云南文山州水资源脆弱性综合评价

利用层次分析法构建符合丰水地区水资源脆弱性评价的指标体系和等级标准,分别构建基于单、双隐层BP神经网络技术的区域水资源脆弱性综合评价模型,并采用内插法构造网络训练样本,将水资源脆弱性分级评价标准值作为“评价”样本,对云南文山州区域水资源脆弱性进行评价分析。结果表明：①单、双隐层BP神经网络模型对区域水资源脆弱性综合评价结果基本相同,说明研究建立的区域水资源脆弱性评价模型和评价方法均是合理可行的,与单隐层网络相比,双隐层网络泛化能力强,预测精度高,但训练时间较长;②文山州各评价区域不同规划水平年水资源脆弱性评价等级为Ⅲ-Ⅴ级,即处于中度脆弱与不脆弱之间,客观反映了该州水资源脆弱性状况,符合区域实际情况。评价结果可以作为研究和评价区域水资源脆弱性的参考依据。     

梯级-关联算法在多泥沙河流含沙量预报中的应用

本文借助历史加成法处理样本数据，并分别利用梯级-关联算法(CC)和误差反馈传播算法(BP)建立模型对黄河下游夹河滩水文站汛期含沙量进行预报。传统BP网络需要预先设定网络结构，预报过程虽利用了神经网络的内插特性，但其样本的处理方式和网络构建方式使得运算效率较低；CC算法仅要求初始网络含有输入层和输出层，通过运算不断向网络增加隐含节点，从而最大限度的减少了在网络构建过程中的主观因素。本文比较了当预报的峰值超出训练样本取值范围时两种算法的表现，结果显示：当预报的峰值为训练样本峰值的2.45倍时，二者均能实现较为准确的预报，BP网络在预报精度上要略高于CC网络，但CC网络在运算速度上要明显快于BP网络。     

河道浅滩演变预测的人工神经网络模型

本文主要介绍利用人工神经网络对河道演变进行预测研究,并选取最佳模型对河道演变进行预测。通过对比分析发现RBF网络模型比BP神经网络模型的训练时间短,两模型的预测精度相差不多。结果证实人工神经网络在河道浅滩演变中具有较强的应用性,为河道浅滩演变预测开辟了一条新思路。     

Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

Managing the groundwater resources is very vital for human life. This research proposes a methodology for predicting the groundwater levels which can be very valuable in water resources management. This study investigates the application of multilayer feed forward network models for forecasting the groundwater values in the region of Montgomery country in Pennsylvania. Multiple training algorithms and network structures were investigated to develop the best model in order to forecast the groundwater levels. Several multilayer feed forward models were created in order to be tested for their performance by changing the network topology parameters so as to find the optimal prediction model. The forecasting models were developed by applying different structures regarding the number of the neurons in every hidden layer and the number of the hidden network layers. The final results have shown a very good forecasting accuracy of the predicted groundwater levels. This research can be very valuable in water resources and environmental management.     

卧倒门承船厢内水面最大升降值神经网络计算模型

 运用人工神经网络原理，在通过大量学习样本对网络进行训练的基础上，建立了卧倒门启闭时升船机承船厢内水面最大升降值的神经网络计算模型（NNCM-MFWS-SLC-TGO）。根据岩滩升船机和三峡升船机的模型试验资料对网络模型的性能进行了测试。测试结果表明，所建立的神经网络模型可用于卧倒门启闭时承船厢内水面最大升降值的初步预测。通过实测，计算结果与测试数据是基本吻合的。     

基于GA-Elman神经网络模型的年径流预测

针对传统神经网络模型静态性及训练算法易陷入局部极值的缺陷,为了实现神经网络训练全局寻优,提高模拟精度,并使网络结构能动态反映年径流系列的时变特性,本文以年降雨及气温作为输入因子、年径流量为模型预测对象,结合遗传算法和Elman神经网络各自的优点,采用遗传算法对网络权值阈值全局优化,通过二者的耦合构建了GA-Elman年径流预测模型。利用构皮滩站1961—2015年的径流系列对模型进行了训练及测试,并对各模型预测性能比较分析。结果表明:GA-Elman模型预测平均相对误差5.29%、均方根误差55.81 mm,效果良好,对于径流预测具有实用价值;神经网络模型预测精度优于基于线性方法的模型,预测平均相对误差从12.01%降至7.07%以下;采用遗传算法改进神经网络权值阈值优化过程,预测平均相对误差从7.07%降低到5.29%,可明显提高模型泛化能力,从而改善径流预测效果。     

基于混沌优化神经网络的农业干旱评估模型

基于人工神经网络拟合函数的原理，建立了计算农业干旱程度的评估模型。通过拟合农业干旱程度的概率分布函数，对农业干旱的概率分布进行了研究。该模型可以在随机变量具体分布未知的情况下，拟合出其概率分布函数。在网络训练方法的选择上，将混沌优化算法和梯度下降法相结合，使计算能够迅速收敛到全局极小点。以河南省濮阳市渠村灌区为例，计算出当地农业干旱程度的概率分布，验证了该模型的有效性，表明该模型能够很好的用于农业干旱的评估。     

Predicting Monsoon Floods in Rivers Embedding Wavelet Transform,Genetic Algorithm and Neural Network

Monsoon floods are recurring hazards in most countries of South-East Asia. In this paper, a wavelet transform-genetic algorithm-neural network model (WAGANN) is proposed for forecasting 1-day-ahead monsoon river flows which are difficult to model as they are characterized by irregularly spaced spiky large events and sustained flows of varying duration. Discrete wavelet transform (DWT) is employed for preprocessing the time series and genetic algorithm (GA) for optimizing the initial parameters of an artificial neural network (ANN) prior to the network training. Depending on different inputs, four WAGANN models are developed and evaluated for predicting flows in two Indian Rivers, the Kosi and the Gandak. These rivers are infamous for carrying large flows during monsoon (June to Sept), making the entire North Bihar of India unsafe for habitation or cultivation. When compared, WAGANN models are found to be better than autoregression models (ARs) and GA-optimized ANN models (GANNs) which use original flow time series (OFTS) for inputs, in simulating river flows during monsoon. In addition, WAGANN models predicted relatively reasonable estimates for the extreme flows, showing little bias for underprediction or overprediction.     

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.     

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.