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.     

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Water Resources Management - This study develops and applies three hybrid models, including wavelet packet-artificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system...     

BP神经网络在水文预报中的应用和展望

简要介绍BP神经网络的发展和特点,从暴雨预报,水位预测,径流分析,水资源配置与管理四个方面综合评述BP神经网络在我国水文预报作业中的应用,并就BP神经网络今后在我国的水文预报作业中的应用进行了研究展望。     

小波人工神经网络在径流预报中的应用

针对径流时间序列的非线性和多时间尺度特性,应用A Trous算法对盘石头水库的月径流序列进行了分析.在此基础上,将小波分析与人工神经网络相结合,建立了组合预测模型,并给出构造模型的一般步骤及关键算法.针对一般BP算法收敛速度慢、易陷入局部极小值的缺陷,提出了基于改进共轭梯度法的BP算法.实践表明:基于小波分析的人工神经网络模型在月径流模拟过程中具有很好的仿真能力,训练后的模型具有较高的精度.     

Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models.     

人工神经网络在径流长期预报中的应用

介绍了人工神经网络（Artificial Neural Network-ANN）模型,并阐述了BP模型算法。通过Matlab6．5中的Neural Network Toolbox编写程序,根据塔里木河源流叶尔羌河卡群控制水文站历年的气象资料和水文资料,对径流量进行了预报,并对模型进行了验证和分析,结果表明：1968～1997年间预报合格的有25年,合格率为83％;1998～2002年的合格率为100％,从而说明神经网络在水文预报方面具有良好的适用性。     

神经网络技术在水文系列中长期预报中的应用

运用人工神经网络技术的基本原理，以降雨量作为基本影响因子，建立了流域年径流量的神经网络预报模型。在建模过程中，为保证计算快速收敛，重新定义了权重增量的计算公式。从两个流域的应用实例资料验证表明，模型基本合理、可靠，具有较好的适应性和预报精度。由模型计算结果可以看出，将人工神经网络技术应用于流域年径流量的预报研究，是以系统的观点将降雨与径流作为输入和输出联系起来，它可为流域径流的中长期变化预测提供一条崭新而有效的途径。     

神经网络在黄河河道断流防治中的应用研究

确保黄河河道不断流,是国家为适应经济社会可持续发展对黄河提出的新要求和新课题。枯季径流条件下断流防治技术是极其复杂的、影响因子众多的、高度非线性映射（因果）关系的问题。在解决这类问题方面,神经网络具有很强的处理大规模复杂非线性动力学系统的能力。将神经网络理论用于河道断流防治的研究,以期识别水流运动变化过程与其影响因子之间的复杂非线性关系,为河道断流防治技术的研究提供解决方案。     

自适应BP神经网络对黄河下游洪水位的预报

利用黄河下游多水文站的多年水位资料,用自适应BP神经网络对艾山水文站的水位进行预测,同时与逐步回归分析以及普通BP神经网络得到的结果对比,结果表明：①自适应BP神经网络的预报精度高于普通BP神经网络、逐步回归法,尤其对最高洪水水位的预报精度有较大的提高;②普通BP神经网络存在易陷入死循环、收敛速度慢、对神经元个数依赖大等缺点,可以利用学习率自适应调整和动量法改进BP神经网络;③建议在补充、完善资料的基础上,将神经网络与时间序列相结合,加强黄河下游洪水水位预报的研究和实验,进一步提高预报精度。     

A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting

Water Resources Management - Reservoir inflow forecasting is one of the most important issues in delicacy water resource management at reservoirs. Considering the non-linearity and of daily...     

基于人工神经网络的黄河源区枯季径流预报

分析了黄河源区枯季径流的影响因素,并选取基于枯季径流形成机理的、与径流相关性高的因子作为人工神经网络模型的输入,运用误差逆传播算法的多层前向网络,利用VC^ 语言调用随机函数克服了网络全局寻优的缺陷,建立了黄河源区枯季径流预报的BP模型。对黄河源区枯季径流长期预报的结果表明,ANN(7,1,15,1)模型预报结果精度高,容错能力强,是枯季径流预报的有效手段。     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

RBF神经网络在阿拉尔垦区需水预测中的应用

利用RBF神经网络,建立了阿拉尔垦区需水量预测模型。选取农业用水灌溉定额、工业用水重复利用率、城镇生活人均日需水量、农村生活人均日需水量作为模型输入,农业、工业、城镇生活、农村生活需水量作为输出。将2001—2007年用水量数据作为训练样本,用2008—2009年用水量数据对模型进行检验。在农业、工业、城镇生活、农村生活4类需水量中,2009年工业需水量预测的相对误差最大,为-16.24%,总需水量的最大误差仅为1.80%,取得了较满意的结果,表明RBF神经网络模型用于该区需水量预测是可行的。     

影响水文神经网络预报精度的因素探讨

在运用神经网络来进行水文预报过程中,采用不同的参数,对预报效果是有影响的.通过对不同参数组合进行计算,来分析不同系列组合、训练系列长度、数据归一化等对神经网络预报效果的影响.研究发现,不同数据系列组合的预报效果有很大的不同,训练系列长度对预报精度是有影响的,训练数据与预报数据之间存在时间、空间的间隔对预报精度的影响是不确定的,输入数据的归一化处理对预报精度的影响与输入数据的分布区间存在一定关系.     

BP神经网络在盘龙河水质综合评价中的应用

基于BP神经网络原理,建立人工神经网络水质综合评价模型,选取影响盘龙河水质类别的总磷、氨氮、高锰酸盐指数等7个指标作为评价因子,并参照GB3838-2002<地表水环境质量标准>,确定神经网络学习和训练样本,运用大型工程计算软件Matlab2010a工具箱中提供的函数进行计算,得到水质的综合评价结果,并将评价结果与单因...     

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Water Resources Management - Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural...     

BP神经网络方法在灌区需水预测中的应用

以江西省廖坊水利枢纽工程灌区为例,通过查阅年鉴、现场调查和专家咨询确定影响需水量的主要因子,根据确定的主要影响因子构造BP神经网络的输入样本,采用Matlab软件平台编程,建立灌区需水模型.利用建立的模型预测2025年灌区的需水量,并与其他方法的预测成果进行比较.结果表明,BP神经网络方法在廖坊水利枢纽工程灌区需水量预测的应用上是成功的.     

Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network

Due to limited data sources, practical situations in most developing countries favor black-box models in real time operations. In a simple and robust approach, this study examines performances of stepwise multiple linear regression (SMLR) and artificial neural network (ANN) models, as tools for multi-step forecasting Chindwin River floods in northern Myanmar. Future river stages are modeled using past water levels and rainfall at the forecasting station as well as at the hydrologically connected upstream station. The developed models are calibrated with flood season data from 1990 to 2007 and validated with data from 2008 to 2011. Model performances are compared for 1- to 5-day ahead forecasts. With a high accuracy, both candidate models performed well for forecasting the full range of flood levels. The ANN models were superior to the SMLR models, particularly in predicting the extreme floods. Correlation analysis was found to be useful for determining the initial input variables. Contribution of upstream data to both models could improve the forecasting performance with higher R ² values and lower errors. Considering the commonly available data in the region as primary predictors, the results would be useful for real time flood forecasting, avoiding the complexity of physical processes.     

基于人工神经网络的大坝位移预测

大坝安全监控中,坝体位移预测以往用回归方法,很难描述位移与诸多影响位移的物理量间的复杂关系。采用人工神经网络方法,对大坝位移进行预测。实例表明,此法比较适用,结果良好。