Improving peak flow estimates in artificial neural network river flow models

In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation. Copyright © 2003 John Wiley & Sons, Ltd.     

Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement.     

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.     

Quantification of the stability of river flow regimes

Abstract

An important characteristic of a river flow regime type is the time of year when high and low flows are likely to occur. How likely is it, however, to observe an identified seasonal pattern each individual year? Stability is an often neglected property of a flow regime, though shifts in the seasonal behaviour of flows affect both environmental and economic activities. An approach to characterize objectively the stability of a flow regime type, based on the concept of entropy, is presented. The stabilities of river flow maxima and minima are studied separately to investigate their respective contributions to the stability character of a particular regime type. A quantitative “instability index” permits a study of the development of a flow regime's stability in time, especially important in the context of a possible climate change. The method is presented using the example of a quantitative flow regime classification developed for Scandinavia and western Europe.     

基于神经网络的地磁观测数据重构研究

在距离数据缺失台站一定范围内选取参考台作为输入,构建非线性BP神经网络并进行地磁观测数据重构研究.数据仿真结果显示,重构数据和原始记录数据吻合程度较高,重构残差较小,磁静日重构平均残差仅为0.11 nT,磁扰日平均重构残差为0.23 nT.重点对磁场活动最剧烈时段内的数据进行了短时重构,平均残差由0.4 nT降低到0.2 nT,重构效果得到较大改进.计算了原始数据与重构数据的功率谱密度,除部分高频信号外,二者变化特征基本相同,相关性高达1.0.从时域和频域验证了BP神经网络在地磁相对记录数据重构上的有效性,并将其运用于实际缺失数据重构,取得较好效果.

     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures, the failure modes of members must be classified. The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER). The main factors affecting failure modes of columns include the hoop ratios, longitudinal reinforcement ratios, ratios of transverse reinforcement spacing to section depth, aspect ratios, axial compression ratios, and flexure-shear ratios....     

A combined rotated general regression neural network method for river flow forecasting

ABSTRACT

This study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R², and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.

Editor D. Koutsoyiannis; Associate editor not assigned     

基于神经网络的滑移隔震结构智能半主动控制

考虑上部结构的刚度和阻尼,使用神经网络控制算法计算基底摩擦力的大小,研究了滑移隔震结构的半主动控制。对计算实例的分析表明,通过半主动控制的滑移隔震结构不但具有较好的隔震效果,且能有效地减小基底的最大滑移量及残余位移。为对比各种控制方法的控制效果,文中还利用Bang-Bang控制和瞬时最优控制算法对滑移隔震结构进行了半主动控制。对比分析表明,基于神经网络控制算法的控制效果优于其它控制算法,具有反馈量少,稳健性强等特点。     

Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models

Abstract

New wavelet and artificial neural network (WA) hybrid models are proposed for daily streamflow forecasting at 1, 3, 5 and 7 days ahead, based on the low-frequency components of the original signal (approximations). The results show that the proposed hybrid models give significantly better results than the classical artificial neural network (ANN) model for all tested situations. For short-term (1-day ahead) forecasts, information on higher-frequency signal components was essential to ensure good model performance. However, for forecasting more days ahead, lower-frequency components are needed as input to the proposed hybrid models. The WA models also proved to be effective for eliminating the lags often seen in daily streamflow forecasts obtained by classical ANN models. 

Editor D. Koutsoyiannis; Associate editor L. See

Citation Santos, C.A.G. and Silva, G.B.L., 2013. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59 (2), 312–324.     

应用人工神经元网络方法自动检测地震事件

利用人工神经元网络方法,提出了一种从连续的地震数据中检测出地震事件的方法。该方法分两步,首先,低阈值的STA／LTA算法从连续的波形中检测出类似地震事件;其次利用神经元网络方法,区分事件是地震事件还是噪声事件。通过对数据检测结果比较,找出了适合地震检测的神经元网络训练方法和神经元传递函数。在对天山流动台阵其中两个台的检测结果表明,在连续约两个月数据中,39RLS台检测出地震75个,30RNA台检测出地震95个,证明该方法对地震事件检测来说是一种有效的方法。     

A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change

ABSTRACT

A forecasting model is developed using a hybrid approach of artificial neural network (ANN) and multiple regression analysis (MRA) to predict the total typhoon rainfall and groundwater-level change in the Zhuoshui River basin. We used information from the raingauge stations in eastern Taiwan and open source typhoon data to build the ANN model for forecasting the total rainfall and the groundwater level during a typhoon event; then we revised the predictive values using MRA. As a result, the average accuracy improved up to 80% when the hybrid model of ANN and MRA was applied, even where insufficient data were available for model training. The outcome of this research can be applied to forecasts of total rainfall and groundwater-level change before a typhoon event reaches the Zhuoshui River basin once the typhoon has made landfall on the east coast of Taiwan.     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.     

基于人工神经网络的城市桥梁震害评估方法

为了使已有的桥梁震害评估方法适用于城市立交桥和高架桥,将桥梁结构是否规则考虑为评估模型的一项输入参数,并剔除了以往桥梁震害评估方法中不合理的影响因素以提高所提方法的可操作性.基于BP神经网络算法,以唐山地震和汶川地震中收集的54座梁式桥的震害资料作为网络训练样本,建立了梁式桥震害评估神经网络模型;通过讨论神经网络模型的泛化能力,得到了较满意的结果,表明该方法具有一定的准确度,可以应用于城市桥梁震害评估工作.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

Muskingum equation based downstream sediment flow simulation models for a river system

Applications of sediment transport and water flow characteristics based sediment transport simulation models for a river system are presented in this study. An existing water–sediment model and a new sediment–water model are used to formulate the simulation models representing water and sediment movement in a river system. The sediment–water model parameters account for water flow characteristics embodying sediment transport properties of a section. The models are revised formulations of the multiple water inflows model describing water movement through a river system as given by the Muskingum principle. The models are applied to a river system in Mississippi River basin to estimate downstream sediment concentration, sediment discharge, and water discharge. River system and the river section parameters are estimated using a revised and the original multiple water inflows models by applying the genetic algorithm. The models estimate downstream sediment transport rates on the basis of upstream sediment/water flow rates to a system. Model performance is evaluated by using standard statistical criteria;downstream water discharge resulting from the original multiple water inflows model using the estimated river system parameters indicate that the revised models satisfactorily describe water movement through a river system. Results obtained in the study demonstrate the applicability of the sediment transport and water flow characteristics-based simulation models in predicting downstream sediment transport and water flow rates in a river system.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series

Stochastic Environmental Research and Risk Assessment - Modeling and forecasting the flow of rivers, especially in flood-prone areas using warning systems, enables officials to take the required...     

Modeling of the river ecological status with macrophytes using artificial neural networks

Biomonitoring methods based on macrophytes have been used mandatorily in the assessment of freshwaters since the implementation of the Water Framework Directive (WFD). The Macrophyte Index for Rivers (MIR) was developed in Poland for the monitoring of running waters under the WFD requirements. This index shows the degree of river degradation under the influence of water pollutants, especially nutrients. The aim of the present study was to determine the relationship between the MIR and various hydrochemical parameters using artificial neural networks (ANNs). Physico-chemical parameters of water (monthly results for the whole year), which were derived from 147 lowland river survey sites, all located in Poland, were applied to model the MIR values. Water quality variables were determined over three timeframes: the annual average; the average for the vegetation period; and the average for the summer period. Quality of the networks was assessed using coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE). The best modeling quality was obtained for yearly average values of water quality parameters. The quality statistics were: R² = 0.722, NSE = 0.721 and RMSE = 0.056 (training dataset); R² = 0.555, NSE = 0.533 and RMSE = 0.101 (validation dataset); R² = 0.650. NSE = 0.600 and RMSE = 0.089 (testing dataset). This indicates that macrophytes reflect the whole year impact of pollution, whereas summer.