Neural Networks for Real Time Catchment Flow Modeling and Prediction

Accurate prediction of catchment flow has been recognized as an important measures for effective flood-risk management strategy. A neural network modeling approach was used to construct a real time catchment flow prediction model for a river basin. Two types of neural network architectures i.e. feed forward and recurrent neural networks, and three types of training algorithm i.e. Levenberg–Marquardt, Bayesian regularization, and Gradient descent with momentum and adaptive learning rate backpropagation algorithms were examined in this study. A total of six different neural network configurations were developed and examined in terms of optimum results for 1 to 5-h ahead prediction. The methods were used to predict flow in the Cilalawi River in Indonesia, and their performances were evaluated using various statistical indices. The modeling results indicate that reasonable prediction accuracy was achieved for most of models for 1-h ahead forecast with correlation >0.91. However, the model accuracy deteriorates as the lead-time increases. When compared, a 4-10-1 recurrent network and 4-4-1 feed forward network, both trained with the Levenberg–Marquardt algorithm has produced a better performances on indicators related to average goodness of prediction for the 1 to 5-h ahead river flow forecasts compared to other models. Feed forward network trained with gradient descent with momentum and adaptive learning rate backpropagation algorithm model appears to be the worst of the adaptive techniques investigated in terms of modeling performances. Thus, the results of the study suggest that recurrent and feed forward network trained with Levenberg–Marquardt are able to forecast the catchment flow up to 5 h in advance with reasonable prediction accuracy.     

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 .

     

River Discharges Forecasting In Northern Iraq Using Different ANN Techniques

The Upper and Lower Zab Rivers are two of main and most important tributaries of Tigris River in Northern Iraq region. They supply Tigris River with more than 40 % of its yield. The forecasting of flows for these rivers is very important in operation of the existing Dokan Dam on the Lower Zab River and the proposed Bakhma Dam on the Upper Zab River for flood mitigation and also in drought periods. Three types of Artificial Neural Networks (ANNs) are investigated and evaluated for flow forecasting of both rivers. The ANN techniques are the feedforward neural networks (FFNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBF). The networks’ performance varied with different cases involved in the study; however, the FFNN was almost better than other networks. The effect of including a time index within the inputs of the networks is investigated. In addition, the ANNs’ performance is investigated in forecasting the high and low peaks and in forecasting river flows using the data of the other river.     

基于小波分析—模糊神经网络的径流预报模型

根据不同的小波分解及重构技术及不同的模糊神经网络模型训练周期,本文提出了四种小波分析与模糊神经网络相结合的径流预报模型,即：基于Mallat算法的母周期径流预报模型、基于Mallat算法的子周期径流预报模型、基于小波包算法的母周期径流预报模型、基于小波包算法的子周期径流预报模型,并阐述了模型建立的原理、结构及步骤。而且,以黄河源区出口水文站——唐乃亥站月径流量为应用实例,采用周期分解系数及模拟效率系数对上述四种模型进行对比评价。结果表明：基于Mallat算法的母周期径流预报模型预报效果最好,基于小波包算法的子周期径流预报模型则模拟效果最差。文中对导致这一现象的主要原因进行了分析, 并对小波分析及模糊神经网络在水文模型中的应用提出了合理化建议。     

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month’s monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE?=?0.0132, MAE?=?0.0883 and R?=?0.8012 statistics, respectively.     

基于神经网络理论的开河期冰坝预报研究

在北方高寒地区的天然河道,开河期冰坝形成和导致凌汛的机理复杂,目前的冰水动力学模型难以模拟和预报其发生、发展和溃决的过程,可用的冰坝预报多采用传统的统计学方法和经验判别式法,为应对严重的防凌形势,迫切需要找到冰坝预报的新方法。本文在对开河期冰坝成因及机理研究的基础上,建立了基于神经网络理论的冰坝预报模型,并将其应用到黑龙江上游凌汛灾害频发的漠河江段冰坝预报中。通过神经网络聚类法预报冰坝是否发生,神经网络聚类法预报精度为85%,高于传统统计学的几率分析法62%的预报精度。通过预报开河日期实现了对冰坝发生时间的预报,开河日期预报平均预见期为10天,最大误差2天,预报合格率100%。该模型提前准确预报2017年黑龙江漠河江段开河冰坝发生情况。及时、准确的冰坝预报能为提前制订主动防凌方案和采取必要防凌措施提供重要的依据。     

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.     

Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach

In this paper, a recursive training procedure with forgetting factor is proposed for on-line calibration of temporal neural networks. The forgetting factor discounts old measurements through an on-line model calibration. The forgetting factor approach enables the recursive algorithm to reduce the effect of the older error data by multiplying the error data by a discounting factor. The proposed procedure is used to calibrate a temporal neural network for reservoir inflow modeling. The mean monthly inflow of the Karoon-III reservoir dam in the south-western part of Iran is used to test the performance of the proposed approach. An autoregressive moving average (ARMA) model is also applied to the same data. The temporal neural network, which is trained with the proposed approach, has shown a significant improvement in the forecast accuracy in comparison with the network trained by the conventional method. It is also demonstrated that the neural network trained with forgetting factor results in better forecasts compared to the statistical ARMA model, which has been calibrated through this approach.     

Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

One of the key elements in achieving sustainable water resources and environmental management is forecasting the future condition of the surface water resources. In this study, the performance of a river flow forecasting model is improved when different input combinations and signal processing techniques are applied on multi‐layer backpropagation neural networks. Haar, Coiflet and Daubechies wavelet analysis are coupled with backpropagation neural networks model to develop hybrid wavelet neural networks models. Different models with different input selections and structures are developed for daily, weekly and monthly river flow forecasting in Ellen Brook River, Western Australia. Comparison of the performance of the hybrid approach with that of the original neural networks indicates that the hybrid models produce significantly better results. The improvement is more substantial for peak values and longer‐term forecasting, in which the Nash–Sutcliffe coefficient of efficiency for monthly river flow forecasting is improved from 0.63 to 0.89 in this study. Copyright © 2014 John Wiley & Sons, Ltd.     

人工神经网络非线性时序模型在水文预报中的应用

首先构造出人工神经网络非线性时序模型 ,然后用该模型进行单变量和多变量时间序列预报研究。为了与传统的随机水文模型对比 ,选择了自回归模型。以日流量序列为例 ,研究结果表明 ,人工神经网络非线性时序模型预报效果不错 ,可以在水文预报中加以应用