Streamflow Forecasting Based on Artificial Neural Networks

Salas, José D.; Markus, Momcilo; Tokar, A. Sezin

doi:10.1007/978-94-015-9341-0_3

Cited by 43 publications

(32 citation statements)

References 17 publications

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“…Salas et al (2000) forecast streamflows with horizonts from one to four months using several NNs, all trained with the error-backpropagation learning algorithm, with successful results overall. Deo and Thirumalaiah (2000) tested different learning algorithms to train multi-layer feedforward networks for time-series modelling of hourly discharges The most extended techniques for synthetic streamflow generation and streamflow forecasting include simple and multiple linear regression, autoregressive moving average (ARMA) models, ARMA with exogenous variables (ARMAX) and ARMA and ARMAX models with periodic parameters.…”

Section: Fig 1 Location Of Entrepeñas and Buendía Reservoirs In Thementioning

confidence: 99%

“…Some nonlinear and non-Gaussian techniques do not need exogenous information and behave better than linear models, as in the case of periodic gamma autoregressive processes PGAR (Fernandez and Salas, 1986), but they are univariate models. Different authors (Lapedes and Farber, 1988;Tang et al, 1991;Zealand et al, 1999;Imrie et al, 2000;and Salas et al, 2000) have tested the capability of certain NN topologies to incorporate complex and non-linear hydrological relationships; they remark on their potentials and abilities as tools for hydrological forecasting.…”

Section: Fig 1 Location Of Entrepeñas and Buendía Reservoirs In Thementioning

confidence: 99%

“…(1) were standardised to improve learning efficiency and overall operation of the NN (Salas et al, 2000). The standardisation was applied on a monthly basis, through equation Finally, an additional transformation was performed on the Y series for a convenient scale of the data to be processed by the NN.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Such techniques have been applied widely for determining the dimensions of hydraulic works for risk asessment in urban water supply systems and irrigation, optimal operation of reservoir systems, planning of new works and actions to optimise hydroelectric production, and others (Salas et al, 1985;Koutsoyiannis, 2000). Time-series prediction has been used in real-time operation, systems operation during drought periods, short term operation strategies in reservoirs and also for flood warning purposes (Salas et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Ochoa-Rivera¹,

García-Bartual²,

Andreu³

2002

Hydrol. Earth Syst. Sci.

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A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation.

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Section: Fig 1 Location Of Entrepeñas and Buendía Reservoirs In Thementioning

confidence: 99%

Section: Fig 1 Location Of Entrepeñas and Buendía Reservoirs In Thementioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Ochoa-Rivera¹,

García-Bartual²,

Andreu³

2002

Hydrol. Earth Syst. Sci.

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show abstract

“…They have been recommended for solving scientific problems (Salas et al, 2000). ANNs can be used if available data are sufficient for modeling complex systems.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

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Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach

Wei

Song

Khan

2011

Hydrological Processes

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Abstract:Accurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high-frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet-neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub-series with low (approximation) and high (details) frequency, and these sub-series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction.

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Streamflow Forecasting Based on Artificial Neural Networks

Cited by 43 publications

References 17 publications

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Simulating and predicting river discharge time series using a wavelet‐neural network hybrid modelling approach

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