共查询到20条相似文献，搜索用时 31 毫秒
1.
Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models.
EDITOR M.C. AcremanASSOCIATE EDITOR R. Hirsch 相似文献
2.
《Advances in water resources》2004,27(7):761-772
The classical deterministic approach to tidal prediction is based on barotropic or baroclinic models with prescribed boundary conditions from a global model or measurements. The prediction by the deterministic model is limited by the precision of the prescribed initial and boundary conditions. Improvement to the knowledge of model formulation would only marginally increase the prediction accuracy without the correct driving forces. This study describes an improvement in the forecasting capability of the tidal model by combining the best of a deterministic model and a stochastic model. The latter is overlaid on the numerical model predictions to improve the forecast accuracy. The tidal prediction is carried out using a three-dimensional baroclinic model and, error correction is instigated using a stochastic model based on a local linear approximation. Embedding theorem based on the time lagged embedded vectors is the basis for the stochastic model. The combined model could achieve an efficiency of 80% for 1 day tidal forecast and 73% for a 7 day tidal forecast as compared to the deterministic model estimation. 相似文献
3.
IOANNIS NALBANTIS 《水文科学杂志》2013,58(2):269-284
Abstract A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes. 相似文献
4.
Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found to give similar results. The results were found to improve significantly by coupling stochastic and deterministic models (tank and NAM) for updating forecast output. The neural network (NN) model was compared with the tank and NAM models. The NN model does not require knowledge of catchment characteristics and internal hydrological processes. The training process or calibration is relatively simple and less time consuming compared with the extensive calibration effort required by the tank and NAM models. The NN model gives good forecasts based on available rainfall, evaporation and runoff data. The black‐box nature of the NN model and the need for selecting parameters based on trial and error or rule‐of‐thumb, however, characterizes its inherent weakness. The performance of the three models was evaluated statistically. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
5.
6.
Scale recursive estimation (SRE) is adopted for short term quantitative precipitation forecast (QPF). The precipitation field is modelled using a lognormal random cascade, well suited to properly represent the scaling properties of rainfall fields. To estimate the random cascade parameters, scale recursive maximum likelihood estimation (MLE) is carried out by the iterative expectation maximization (EM) algorithm. To illustrate the potentiality of the SRE, forecast of a synthetically generated rainfall time series is shown. Adaptive estimation of the process parameters is carried out and precipitation forecasts are issued. The forecasts from the SRE are compared with those from standard ARMA models, showing a good performance. The SRE is then adopted for forecasting of an observed half hourly precipitation series for a two day storm event in northern Italy. The SRE provides good performance and it can therefore be adopted as a tool for short term QPF. 相似文献
7.
Majid Dehghani Bahram Saghafian Firoozeh Rivaz Ahmad Khodadadi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(3):861-874
In this research, a dynamic linear spatio-temporal model (DLSTM) was developed and evaluated for monthly streamflow forecasting. For parameter estimation, coupled expectation–maximization (EM) algorithm and Kalman filter was adopted. This combination enables the model to estimate the state vector and parameters concurrently. Different forecast scenarios including various combinations of upstream stations were considered for downstream station streamflow forecasting. Several statistical criteria, nonparametric and visual tests were used for model evaluation. Results indicated that the spatio-temporal model performed acceptably in almost all scenarios. The dynamic model was able to capitalize on coupled spatial and temporal information provided that there is spatial connectivity in the studied hydrometric stations network. Moreover, threshold level method was used for model evaluation in drought and wet periods. Results indicated that, in validation phase, the model was able to forecast the drought duration and volume deficit/over threshold, although volume deficit/over threshold could not be accurately simulated. 相似文献
8.
A problem frequently met in engineering hydrology is the forecasting of hydrological variables conditional on their historical observations and the hindcasts and forecasts of a deterministic model. On the contrary, it is a common practice for climatologists to use the output of general circulation models (GCMs) for the prediction of climatic variables despite their inability to quantify the uncertainty of the predictions. Here we apply the well-established Bayesian processor of forecasts (BPF) for forecasting hydroclimatic variables using stochastic models through coupling them with GCMs. We extend the BPF to cases where long-term persistence appears, using the Hurst-Kolmogorov process (HKp, also known as fractional Gaussian noise) and we investigate its properties analytically. We apply the framework to calculate the distributions of the mean annual temperature and precipitation stochastic processes for the time period 2016–2100 in the United States of America conditional on historical observations and the respective output of GCMs. 相似文献
9.
ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) 总被引：1，自引：0，他引：1
Ujjwal Kumar V. K. Jain 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):751-760
In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has
been adapted to forecast daily mean ambient air pollutants (O3, CO, NO and NO2) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied
to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions,
namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion),
and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function)
inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q)
models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean
absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts,
one step (i.e., one day) ahead MAPE for CO, NO2, NO and O3, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in
the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach
is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning
purposes. 相似文献
10.
Drought forecasting using stochastic models 总被引：8，自引：4，他引：8
A. K. Mishra V. R. Desai 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(5):326-339
Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment
and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as
stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures
in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems.
In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average
(SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast
droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia
district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed
data. The predicted results show reasonably good agreement with the actual data, 1–2 months ahead. The predicted value decreases
with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy. 相似文献
11.
A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
12.
Drought is one of the most devastating climate disasters. Hence, drought forecasting plays an important role in mitigating some of the adverse effects of drought. Data-driven models are widely used for drought forecasting such as ARIMA model, artificial neural network (ANN) model, wavelet neural network (WANN) model, support vector regression model, grey model and so on. Three data-driven models (ARIMA model; ANN model; WANN model) are used in this study for drought forecasting based on standard precipitation index of two time scales (SPI; SPI-6 and SPI-12). The optimal data-driven model and time scale of SPI are then selected for effective drought forecasting in the North of Haihe River Basin. The effectiveness of the three data-models is compared by Kolmogorov–Smirnov (K–S) test, Kendall rank correlation, and the correlation coefficients (R2). The forecast results shows that the WANN model is more suitable and effective for forecasting SPI-6 and SPI-12 values in the north of Haihe River Basin. 相似文献
13.
Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series
《水文科学杂志》2013,58(4):588-598
Abstract The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS. 相似文献
14.
Impact of complexity on daily and multi-step forecasting of streamflow with chaotic,stochastic, and black-box models 总被引：1，自引：0，他引：1
Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models. 相似文献
15.
Abstract Time series modelling approaches are useful tools for simulating and forecasting hydrological variables and their change through time. Although linear time series models are common in hydrology, the nonlinear time series model, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, has rarely been used in hydrology and water resources engineering. The GARCH model considers the conditional variance remaining in the residuals of the linear time series models, such as an ARMA or an ARIMA model. In the present study, the advantages of a GARCH model against a linear ARIMA model are investigated using three classes of the GARCH approach, namely Power GARCH, Threshold GARCH and Exponential GARCH models. A daily streamflow time series of the Matapedia River, Quebec, Canada, is selected for this study. It is shown that the ARIMA (13,1,4) model is adequate for modelling streamflow time series of Matapedia River, but the Engle test shows the existence of heteroscedasticity in the residuals of the ARIMA model. Therefore, an ARIMA (13,1,4)-GARCH (3,1) error model is fitted to the data. The residuals of this model are examined for the existence of heteroscedasticity. The Engle test indicates that the GARCH model has considerably reduced the heteroscedasticity of the residuals. However, the Exponential GARCH model seems to completely remove the heteroscedasticity from the residuals. The multi-criteria evaluation for model performance also proves that the Exponential GARCH model is the best model among ARIMA and GARCH models. Therefore, the application of a GARCH model is strongly suggested for hydrological time series modelling as the conditional variance of the residuals of the linear models can be removed and the efficiency of the model will be improved. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Modarres, R. and Ouarda, T.B.M.J., 2013. Modelling heteroscedasticty of streamflow times series. Hydrological Sciences Journal, 58 (1), 1–11. 相似文献
16.
与传统确定性预报相比,洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区,建立新安江次洪模型,并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上,建立基于水文不确定性处理器（HUP）的次洪概率预报模型,定量分析预报不确定性,实现入库洪水概率预报。结果表明：(1)利用猴子岩流域2009 2019年水文气象资料,建立的新安江次洪模型整体精度较高,率定期和验证期的洪量和洪峰相对误差均在±20%以内,平均确定性系数分别为0.69和0.72;经动态系统响应曲线校正后,洪峰和洪量误差均有降低,率定期和验证期的确定性系数分别提高0.13和0.09。(2)以2020年3场洪水未来48 h预报降雨为输入,新安江模型预报精度不高,且随着预见期增长而降低,但经动态系统响应曲线校正后,整体预报精度有所提高,洪量相对误差减小幅度超50%,确定性系数提高幅度超60%。(3)HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报;提供的90%置信区间覆盖率均在90%左右,离散度均在0.40以下,能以相对较窄的区间覆盖大部分实测值... 相似文献
17.
The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error. 相似文献
18.
Arne Melsom Fran?ois Counillon Joseph Henry LaCasce Laurent Bertino 《Ocean Dynamics》2012,62(8):1245-1257
We investigate trajectory forecasting as an application of ocean circulation ensemble modeling. The ensemble simulations are performed weekly, starting with assimilation of data for various variables from multiple sensors on a range of observational platforms. The ensemble is constructed from 100 members, and member no. 1 is designed as a standard (deterministic) simulation, providing us with a benchmark for the study. We demonstrate the value of the ensemble approach by validating simulated trajectories using data from ocean surface drifting buoys. We find that the ensemble average trajectories are generally closer to the observed trajectories than the corresponding results from a deterministic forecast. We also investigate an alternative model in which velocity perturbations are added to the deterministic results and ensemble mean results, by a first-order stochastic process. The parameters of the stochastic model are tuned to match the dispersion of the ensemble approach. Search areas from the stochastic model give a higher hit ratio of the observations than the results based on the ensemble. However, we find that this is a consequence of a positive skew of the area distribution of the convex hulls of the ensemble trajectory end points. 相似文献
19.
Ugo Moisello 《水文研究》2002,16(13):2667-2684
The maximum depth of a river section is schematized as a non‐stationary continuous‐parameter continuous stochastic process, with a three‐parameter lognormal distribution. Two processes, represented by a first‐order and a second‐order differential equation, are considered. Non‐stationarity is accounted for by the mean, the other parameters being assumed constant. The continuous processes are then discretized as AR(1) and ARMA(2,1) processes respectively, and used for computing the conditional probability (which is of practical interest) for a given maximum depth not to be exceeded in a period of given length. The models are applied to the River Po (Italy) and the AR(1) model is found to be preferable. An analysis of the effect of discretizing the parameter is also carried out, considering the second‐order model and the conditional probability, for which analytical results for the continuous‐parameter model are available. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
20.
《国际泥沙研究》2022,37(6):766-779
Sediment forecasting at a dam site is important for the operation and management of water and sediment in a reservoir. However, the forecast results generally have some uncertainties, which may hinder the operation of the dam. In this study, a real-time sediment concentration probabilistic forecasting model is proposed based on a dynamic network model. Under this framework, the Elman neural network (ENN) and nonlinear auto-regressive with exogenous inputs (NARX) neural network models were established for sediment concentration forecasting with different lead times. A hybrid algorithm, which combined the Levenberg–Marquardt algorithm and real-time recurrent learning, was used to train the model. Using the aforementioned method, the sediment concentration was forecast for at the Sanmenxia Dam, China, and, subsequently, the forecast results were evaluated. Among the selected lead time, the results at 5 h exhibited the highest accuracy and practical significance. Compared with the ENN model, the sediment concentration peak error using the NARX neural network was reduced by 4.5%, and the sediment yield error was reduced by 0.043%. Therefore, the NARX neural network was selected as the deterministic sediment forecasting model. Additionally, the probability density function of the sediment concentration was derived based on the heterogeneity of the error distribution, and the sediment concentration interval, with different confidence levels, expected values, and median values, was forecast. The Nash–Sutcliffe coefficient of efficiency for the sediment concentration, as forecasted based on the median value, was the highest (0.04 higher than that using a deterministic model), whereas the error of the sediment concentration peak and sediment yield remained unaltered. These results indicated the accuracy and superiority of the proposed real-time sediment probabilistic forecasting hybrid model. 相似文献