ANNs have been widely applied in SF modelling and prediction for a multiplicity of objectives in the two last decades. Nilsson et al. (2006) investigated the opportunity of modelling monthly Sf for two Norwegian river basins using ANNs and conceptual runoff modelling (CM). ANNs offered the best predictions of monthly SF for both basins with
R2 of 0.82 and 0.71, respectively. Thereafter, they used a combination of ANNs and CM
improve the modelling performance. The R2 for both basins was improved to 0.86 and
0.75, respectively.
Nayebi et al. (2006) employed ANNs-based models for daily SF prediction in the upper
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effect of minimum air temperature on the modelling performance. They developed four ANNs-based models with deferent combination of input variables for SF prediction. The models with minimum air temperature as input variables achieved the best performance in SF prediction.
Sahoo and Ray (2006) applied ANNs for SF prediction of a Hawaii stream in Hawaii island, USA. The predicted SF by ANNs-based models were compared to SF estimated by conventional rating curves which estimated by the United States Geological Survey, the results verified that ANNs-based models outclass the conventional rating curves in SF prediction.
Sahoo et al. (2006) applied ANNs to evaluate flash floods and their associated with water quality specifications using observed data of a Hawaii stream in Hawaii island, USA. They demonstrated that ANNs can predict SF, turbidity and specific conductance with high R although they didn’t reached good results in prediction of dissolved oxygen, PH and water temperature.
Pulido-Calvo and Portela (2007) employed ANNs for one-step ahead daily SF forecasting in some Portuguese basins. They applied many ANNs-based models with several inputs combinations such the flow in previous days. The models with inputs of three previous days flow combined verified very high performance. Generally the work proved that it is potential to reach accurate daily SF predictions using ANNs, even with inadequate data. Aqil et al. (2007) utilized two types of ANNs namely, feed forward and recurrent NN with three types of training algorithm in real-time SF prediction. ANNs-based models were developed and evaluated based on results for 1 to 5-h ahead prediction in the Cilalawi River in Indonesia. According to results, high performance was reached for most of models for 1-h ahead with R around 0.91. However, the model performance declines
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with increasing the lead-time, the results suggested that recurrent and feed forward network are capable to predict the SF up to 5 hr in advance with high accuracy.
Jain and Kumar (2007) developed a new hybrid time series NN model contains an overall modelling framework. It integrates between the conventional and ANNs, it was tested using the monthly SF data in Colorado River in Lees Ferry, USA. The results suggested that the proposed approach provided a strong modelling tool able to capture the non- linearity of the hydrological time series and accordingly generating more accurate predictions.
Ahmed and Sarma (2007) generated SF data of the Pagladia River in Assam, India by ANNs. They compared ANNs with other existing models such as autoregressive moving average (ARMA) model and Thomas-Fiering model. The comparison conducted based on five different statistics of the historical data and synthetically generated data. ANNs demonstrated the highest performance in generating SF data.
Gopakumar et al. (2007) explored the applicability of ANNs for prediction of daily SF in the Achencoil River basin un India. Although the developed ANNs model revealed high performance for rainy period, the performance for the low flow period didn’t revealed same performance. To improve ANNs-based models performance’, the modelling data were analyzed using Self Organizing Maps (SOM). The new approach for SF modelling utilizing the result of SOM analysis enhanced the model performance of daily SF prediction.
Singh and Kumar (2007) proposed the application of ANNs to estimate the missing mean monthly SF of Narmada River in India. The performance of ANNs-based model was compared with the Langbein's log deviation method and provided an adequate alternate to this method.
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Turan and Yurdusev (2009) employed both of FFBP, GRNN and fuzzy logic to estimate missing SF records by the records of the four SF gauge upstream stations in the Birs River in Switzerland. The performances of these models were evaluated to select the best fit model. Based on the performance evaluation, the three modelling techniques demonstrated acceptable performance. However, FFBP outperforms other models. Kentel (2009) applied ANNs for estimation of monthly SF. They used previous RF, SF, and the related month as input variables to forecast SF of Guvenc River in Turkey. They studied the influences of input vectors, number of training trails, and initial weights of the connections of NN on the ANNs-based models performance. ANNs achieved promising results in SF prediction, making it an adequate alternative SF prediction technique. Rakhshanehroo et al. (2010) applied ANNs for flood prediction in similar basins in Iran. ANNs was trained as an event-based modelling tool utilizing data from only 2 of the basins. The prediction performance then evaluated for all basins, high prediction accuracy was reached. They came to conclusion that the ANNs model may be utilized for flood prediction in similar basin with accepted accuracy.
Besaw et al. (2010) developed ANNs-based model to predict SF in ungauged basins from sub-basins in Northern Vermont, USA. They employed time-lagged records of RF and temperature as input variables of the ANNs-based model. Time series analysis of the climate Q data offers an efficient method to decide the suitable steps number of time- lagged input variables. The results suggested that the proposed methods are appropriate to predict Q in ungagged basin and also it was shown that Q prediction was superior to those using daily records for the small river basins.
Sahu et al. (2011) applied ANNs for forecasting SF in open channel flow. They compared the performance of ANNs-based model with four widely used approaches. The results showed that the ANNs-based model is superior to other models in SF prediction.
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Machado et al. (2011) explored the ability of ANNs-based model to predict the monthly RF-SF in the Jangada River basin, Paraná, Brazil. The prediction accuracy of ANNs- based model was compared to those of a conceptual-based model. The ANNs-based model achieved the higher performance based on some statistical performance evaluation criteria such as R and Nash-Sutcliffe statistics.
Tiwari et al. (2012) proposed a novel modelling approach for prediction of daily SF using neural units with higher-order synaptic operations (NU-HSO). They compared between ANNs-based models with NU-HSO and conventional ANNs-based models. The prediction process was performed using 1- to 5-day lead time prediction in the Mahanadi River basin at the Naraj gauging station. According to results, ANNs-based models with NU-HSO achieved higher performance than conventional ANNs-based models based on some statistical performance evaluation criteria such as R and Nash-Sutcliffe statistics. Thus, this results shows that ANNs-based models with NU-HSO can be an adequate alternative SF prediction technique.
Tiwari et al. (2013) employed self-organising maps (SOM) to homogeneously classify the data sets of four types of ANNs-based models developed for daily SF predictions. The four types are traditional ANNs, wavelet-based NN (WNN), bootstrap-based NN (BNN) and wavelet-bootstrap-based NN (WBNN). According to results of pridection process, the SOM’s efficiency in clasfication of data into different clusters were noticed through enhancing the accuracy and reliability of daily SF prediction.
Wei et al. (2013) developed WNN hybrid modelling approach for monthly SF prediction in the Weihe River, China. Monthly SF records from three stations were employed to train and test the model for 48-month-ahead prediction. The prediction results using WNN achieved high enhancement in the model performance in comparison to the results
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Sahay and Srivastava (2014) developed a wavelet transform-genetic algorithm-neural network model (WAGANN) for prediction 1-day-ahead monsoon SF. Discrete wavelet transform (DWT) was used for preprocessing the time series and genetic algorithm (GA) for optimizing the initial parameters of an ANNs to the NN training. Four WAGANNs models with different combination of inputs variables are developed for prediction of SF in two Indian Rivers, the Kosi and the Gandak. According to results, WAGANNs models domestrated better pridection accuracy than autoregression models (ARs) and GA- optimized ANNs based models which use original SF time series for inputs variables. Elsafi (2014) employed ANNs to predict SF in the Nile River at Dongola Station in Sudan. Readings from stations along the Blue Nile, White Nile, Main Nile, and River Atbara from the period between 1965 and 2003 were employed in the modelling process. The results showed that the ANNs model may be utilized for flood prediction in the Nile River with high accuracy.