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1.
Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
2.
Ground motion prediction is important for earthquake early warning systems, because the region’s peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and precisely with limited station wave records, we propose a real-time numerical shake prediction and updating method. Our method first predicts the ground motion based on the ground motion prediction equation after P waves detection of several stations, denoted as the initial prediction. In order to correct the prediction error of the initial prediction, an updating scheme based on real-time simulation of wave propagation is designed. Data assimilation technique is incorporated to predict the distribution of seismic wave energy precisely. Radiative transfer theory and Monte Carlo simulation are used for modeling wave propagation in 2-D space, and the peak ground motion is calculated as quickly as possible. Our method has potential to predict shakemap, making the potential disaster be predicted before the real disaster happens. 2008 M S8.0 Wenchuan earthquake is studied as an example to show the validity of the proposed method. 相似文献
3.
Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam,Iran 总被引：1，自引：1，他引：0
Khalil Rezaei Bernard Guest Anke Friedrich Farajollah Fayazi Muhammad Nakhaei Ali Beitollahi Seyed Mahmoud Fatemi Aghda 《Acta Geophysica》2009,57(2):271-293
An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data
from Bam city is presented. A feed forward back propagation network was developed employing 45 sets of input data. The input
variables in the ANN model are the electrical resistivity, water table as a Boolean value and depth; the output is the mean
grain size. To demonstrate the authenticity of this approach, the network predictions are compared with those from interpolation
methods and the same data. This comparison shows that the ANN approach performs better results. The predicted and observed
mean grain size values were compared and show high correlation coefficients. The ANN approach maps show a high degree of correlation
with well data based grain size maps and can therefore be used conservatively to better understand the influence of input
parameters on sedimentological predictions. 相似文献
4.
ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems 总被引：1，自引：0，他引：1
The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region. 相似文献
5.
Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models 总被引：1，自引：1，他引：0
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. 相似文献
6.
A new methodology for the extension of the impact of data assimilation on ocean wave prediction 总被引：1，自引：1，他引：0
It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting
the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing”
the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues
to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited
portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination
of two different statistical tools—Kolmogorov–Zurbenko and Kalman filters—is employed so as to eliminate any systematic error
of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated
to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific
Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The
results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to
a significant reduction of the forecast bias. 相似文献
7.
This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained. 相似文献
8.
ABSTRACTThe application of artificial neural networks (ANNs) has been widely used recently in streamflow forecasting because of their ?exible mathematical structure. However, several researchers have indicated that using ANNs in streamflow forecasting often produces a timing lag between observed and simulated time series. In addition, ANNs under- or overestimate a number of peak flows. In this paper, we proposed three data-processing techniques to improve ANN prediction and deal with its weaknesses. The Wilson-Hilferty transformation (WH) and two methods of baseflow separation (one parameter digital filter, OPDF, and recursive digital filter, RDF) were coupled with ANNs to build three hybrid models: ANN-WH, ANN-OPDF and ANN-RDF. The network behaviour was quantitatively evaluated by examining the differences between model output and observed variables. The results show that even following the guidelines of the Wilson-Hilferty transformation, which significantly reduces the effect of local variations, it was found that the ANN-WH model has shown no significant improvement of peak flow estimation or of timing error. However, combining baseflow with streamflow and rainfall provides important information to ANN models concerning the flow process operating in the aquifer and the watershed systems. The model produced excellent performance in terms of various statistical indices where timing error was totally eradicated and peak flow estimation significantly improved.
Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei 相似文献
9.
Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally
require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain
for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes
the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport
and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here
for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described
here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information
about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation
problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the
method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption.
First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that
information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from
satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt
in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable
emission rate. 相似文献
10.
Özgür Kişi 《水文研究》2009,23(2):213-223
This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi‐layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens‐Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
11.
AbstractThe accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time. 相似文献
12.
13.
A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height (H s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model–data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point. 相似文献
14.
《中国科学D辑(英文版)》2006,(Z2)
The nonlinearity of the relationship between CO2 flux and other micrometeorological variables flux parameters limits the applicability of carbon flux models to accurately estimate the flux dynamics. However, the need for carbon dioxide (CO2) estimations covering larger areas and the limitations of the point eddy covariance technique to address this requirement necessitates the modeling of CO2 flux from other micrometeorological variables. Artificial neural networks (ANN) are used because of their power to fit highly nonlinear relations between input and output variables without explaining the nature of the phenomena. This paper applied a multilayer perception ANN technique with error back propagation algorithm to simulate CO2 flux on three different ecosystems (forest, grassland and cropland) in ChinaFLUX. Energy flux (net radiation, latent heat, sensible heat and soil heat flux) and temperature (air and soil) and soil moisture were used to train the ANN and predict the CO2 flux. Diurnal half-hourly fluxes data of observations from June to August in 2003 were divided into training, validating and testing. Results of the CO2 flux simulation show that the technique can successfully predict the observed values with R2 value between 0.75 and 0.866. It is also found that the soil moisture could not improve the simulative accuracy without water stress. The analysis of the contribution of input variables in ANN shows that the ANN is not a black box model, it can tell us about the controlling parameters of NEE in different ecosystems and micrometeorological environment. The results indicate the ANN is not only a reliable, efficient technique to estimate regional or global CO2 flux from point measurements and understand the spatiotemporal budget of the CO2 fluxes, but also can identify the relations between the CO2 flux and micrometeorological variables. 相似文献
15.
We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions. 相似文献
16.
富营养化模型是进行湖泊水环境质量预测和管理的重要工具,然而模型客观存在的误差一直是应用者关心的重要问题.数据同化作为连接观测数据与数值模型的重要方法,可以有效提高模型的准确性.集合卡尔曼滤波(En KF)是众多数据同化算法中应用最为广泛的一种,可进行非线性系统的数据同化,并能有效降低数据同化的计算量.本研究以太湖作为具体实例,选择Delft3D-BLOOM作为富营养化模型,在数值实验确定En KF集合数为100、观测误差方差为1%、模拟误差方差为10%的基础上分别进行模型状态变量同化以及状态变量与关键参数同步同化.结果显示,仅同化状态变量时,模型预测精度有所增加;同时同化状态变量和关键参数时,可显著提升模型在湖泊水环境质量预测中的精度.该研究为应用集合卡尔曼滤波以提高复杂的湖库富营养化模型模拟精度提供了有效的方法. 相似文献
17.
《地震工程与工程振动(英文版)》2015,(3)
Analytical models prepared from field drawings do not generally provide results that match with experimental results.The error may be due to uncertainties in the property of materials,size of members and errors in the modelling process.It is important to improve analytical models using experimentally obtained data.For the past several years,data obtained from ambient vibration testing have been successfully used in many cases to update and match dynamic behaviors of analytical models with real structures.This paper presents a comparison between artificial neural network(ANN) and eigensensitivity based model updating of an existing multi-story building.A simple spring-mass analytical model,developed from the structural drawings of the building,is considered and the corresponding spring stiffness and lumped mass of all floors are chosen as updating parameters.The advantages and disadvantages of these updating methods are discussed.The advantage is that both methods ensure a physically meaningful model which canbe further employed in determining structural response and health monitoring. 相似文献
18.
An effective method based on dynamic sampling for data assimilation in a global wave model 总被引：3，自引：2，他引：1
The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation. 相似文献
19.
Observations on the application of artificial neural network to predicting ground motion measures 
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下载免费PDF全文 Application of the artificial neural network (ANN) to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure. 相似文献
20.
Typhoon-induced waves and surges are important when predicting potential hazards near coastal regions. In this paper, we applied a coupled modeling system for ocean–wave interaction to examine prediction capabilities for typhoon-induced waves and surges around the Korean Peninsula. To identify how ocean–wave coupling impacts wave and surge simulations during typhoon conditions, a set of comparative experiments was performed during Typhoon Bolaven (2012): (1) a fully coupled ocean–wave model, (2) a one-way coupled ocean–wave model without surface current feedback and ocean-to-wave water levels, and (3) a stand-alone ocean model without considering wave-based sea surface roughness (SSR). When coupled with the ocean model, the surface current reduced significantly the wave height on the right-hand side of the advancing typhoon track and improved prediction accuracy along the southern coast of Korea. Compared with the observed surge levels, the simulated surge height yielded improved results for peak height magnitude and timing compared with the uncoupled model. For wave-to-surge feedback, we found that wave-induced SSR plays an important role by modulating wind stress in the surface layer. The modulated wind stress directly affected the surge height, which improved surge peak prediction during the typhoon. 相似文献