Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of<scp>Ç</scp>oruh basin,<scp>T</scp>urkey

Can, İbrahim; Tosunoğlu, Fatih; Kahya, Ercan

doi:10.1111/j.1747-6593.2012.00337.x

Cited by 32 publications

(15 citation statements)

References 18 publications

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“…rainfall-runoff modeling [1,26,6,8], reservoir inflow forecasting [22], stream flow prediction [5,7,19,25,18], sea level prediction [15], water level fluctuations [4,29] and rainfall prediction [27,3,11,2,10,9,21]. Based on these research outcomes ANNs could be appropriate method to simulate and forecasting.…”

Section: Introductionmentioning

confidence: 99%

Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model

Farajzadeh¹,

Fard

Lotfi³

2014

Water Resources and Industry

107

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a b s t r a c tUrmia lake basin located in northwestern Iran is the second largest saline lake in the world. Due to many reasons i.e. climate changes, several dam constructions, building a bridge across the Lake, extra agricultural consumption and improper management of water resources, the water level of the lake has been decreased since 1997 and thousand hectares of emerged salty land has made numerous ecological and environmental problems. Therefore, an accurate forecast of the entrance runoff to the lake is important in managing the river flow and water transfer within basins. There are various methods for time-series based forecasting; in the presented study Feed-forward Neural Network and Autocorrelation Regressive Integrated Moving Average (ARIMA) models were applied to forecast the monthly rainfall in Urmia lake basin. The results showed that the estimated values of monthly rainfall through Feed-forward NN were close to ARIMA model with coefficient of correlation 0.62 and the root mean square error of 12.43 mm over the 6 years test period; then rainfall amount were predicted for a 6-year period starting from

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Section: Introductionmentioning

confidence: 99%

Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model

Farajzadeh¹,

Fard

Lotfi³

2014

Water Resources and Industry

107

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“…Continuous streamflow records in such cases can provide systematic prediction models using time series analysis. In recent literature, due to advances in computing systems, application of artificial intelligence (AI) techniques for cross-station or singlestation daily or monthly streamflow prediction has been investigated, and successful results have been reported (Ochoa-Rivera et al 2002;Kisi and Cigizoglu 2007;Kisi 2008;Demirel et al 2009;Toprak et al 2009;Besaw et al 2010;Can et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Mehr

Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Ç oruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-meansquare error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

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“…Different types of ANN have been used in hydrological modelling such as radial basis function (Dawson et al ., ; Moradkhani et al ., ; Nor et al ., ; Lin and Wu, ), Bayesian neural networks (Kingston et al ., ; Khan and Coulibaly, ; Jiang et al ., ) and feedforward multi‐layer perception, which is the most popular neural network paradigm in hydrological forecasting (Sivakumar et al ., ; Cigizoglu, ; Kim and Valdés, ; Kumar et al ., ; Srinivasulu and Jain, ; Machado et al ., ; Weilin et al ., , Can et al ., ). The results of these studies indicate that ANN is a reliable approach for reaching accurate and quick forecasts.…”

Section: Introductionmentioning

confidence: 97%

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

Badrzadeh

Sarukkalige

Jayawardena

2014

River Research & Apps

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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. Figure 3. Nash-Sutcliffe coefficients for (a) training and (b) validation sets, for different backpropagation neural network and wavelet neural network (WNN) models. ANN, artificial neural network. This figure is available in colour online at wileyonlinelibrary.com/journal/rra FLOW FORECASTS WITH SIGNAL PROCESS TECHNIQUE 249 Figure 5. Scatter plots of observed and forecasted river flow with the best-fitted backpropagation neural network (BPNN) and wavelet neural network (WNN) models for one step ahead daily, weekly and monthly forecasting. This figure is available in colour online at wileyonlinelibrary.com

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Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study ofÇoruh basin,Turkey

Cited by 32 publications

References 18 publications

Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model

Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model

Successive-station monthly streamflow prediction using different artificial neural network algorithms

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

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