SVM Applications in Stream Flow Modelling

Given that it is comparatively new, SVM is not as widely applied as ANNs, although it has been applied in several SF applications. However, recent literature provides some applications of SF modelling and prediction using two or more AI techniques (i.e., SVM and ANNs) to improve the performance of the modelling process.

Tirusew Asefa et al. (2006) applied SVM for SF predictions in arid regions based on two time scales: seasonal SF and Q. The results of these models revealed a good efficiency in explaining spatial and temporal SF process. SF was predicted using local-climatological data and demanding less input variables than process-based models. Seasonal SF prediction was also enhanced by integrating atmospheric circulation indicators in the modelling process.

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Lin et al. (2006) applied SVM for long-term SF prediction. They used a shuffled complex evolution algorithm to detect the suitable specifications of the SVM-based model. The SVM prediction model was evaluated by the long-term monthly SF records of the Manwan Hydropower station, Lancang River in Yunnan Province, China. They compared the performance of SVM-based model with ARMA and ANNs-based models, the results verified that SVM could be considered as a very promising tool in long-term SF prediction.

Chen and Yu (2007) applied SVM in real-time flood prediction in Lan-Yang River, Taiwan. They used the cross-correlation technique to select the input variables of the SVM-based models. The real-time prediction performance was evaluated, the results indicated that the SVM is a probable prediction technique in SF.

Behzad et al. (2009) investigated the ability of SVM, ANNs, and ANNs integrated with GA (ANNs–GA) models to predict one-day lead SF of the Bakhtiyari River in Iran. They used local climate and RF records in the modelling process. The results proved that the SVM-based model outperforms the ANNs and ANNs–GA in predicting one-day lead SF. Noori et al. (2011) explored the ability of some preprocessing techniques (i.e. principal component analysis (PCA), Gamma test (GT), and forward selection (FS) in improving the monthly SF prediction by SVM in the Sofichay River, 120 km to Tabriz southwest, Iran. They used 18 input variables, such as monthly RF, discharge, sun radiation, and temperature (as minimum, maximum and mean) with three temporal lags belong to t, t-1, and t-2. Consequently, PCA, GT, and FS techniques were applied to decrease the input variables from 18 to 5 by PCA and GT, and to 7 by FS. Results showed that the performance of the improved SVM-based model (i.e. PCA-SVM and GT-SVM) models outperform the conventional SVM-based model. R2 _{between the observed and predicted}

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SF for PCA-SVM based model was equal to 0.92 and 0.88 in the training and testing data sets, respectively.

Guo et al. (2011) applied SVM for monthly SF prediction. They used adaptive insensitive factor to improve the performance of SVM-based model and the wavelet denoise method to minimize the noise in SF data. The performance of the SVM-based model is explored and compared with the performance of ANNs-based model. The results verified that the improved SVM-based model is of better generalization capability and prediction accuracy than ANNs-based models.

Samsudin et al. (2011) proposed a novel hybrid prediction model for monthly SF, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). They applied GMDH to determine the useful input variables for the LSSVM model. Monthly SF data from two stations, the Selangor and Bernam Rivers in Selangor state of Peninsular Malaysia were employed in the modelling process. The performance of the new hybrid model was compared with ANNs, Autoregressive Integrated Moving Average (ARIMA). RMSE and R were used to evaluate the models’ performances. The new hybrid model has been found to provide more accurate prediction compared to the other models.

Shabri and Suhartono (2012) applied SVM for monthly SF prediction in the Kinta River in Perak, Malaysia. They investigated the capability of a least-squares support vector machine LSSVM model to enhance the performance of SF prediction. They applied Cross-validation and grid-search techniques to determine the model variables. The accuracy of the LSSVM model was compared with the conventional statistical ARIMA, ANNs and conventional SVM models. According to results, the LSSVM model outclasses the other modelling techniques and it could be employed effectively in SF prediction.

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Kisi et al. (2012) evaluated the performance of some AI techniques such as ANFIS, ANNs and SVM in prediction daily SF in two stations in north-western Turkey. They also compared the performance of the three models with two linear regression models. The results showed that the ANFIS, ANNs and SVM are superior in prediction of daily SF. Kalteh (2013) applied two AI techniques (i.e., SVM and ANNs) in prediction of monthly SF of Kharjegil and Ponel stations in Iran. He also coupled the SVM and ANNs with the wavelet transform to improve the modelling performance. According to results, both ANNs and SVM coupled with wavelet transform, provided more precise prediction than the traditional ANNs and SVM. However, it is noticed that SVM coupled with wavelet transform provided better prediction than ANNs coupled with wavelet transform. The results also indicated that traditional SVM outperform slightly better than traditional ANNs.

Ch et al. (2013), investigated the ability of the hybrid model (support vector machine- quantum behaved particle swarm optimization) SVM-QPSO in forecasting monthly SF of Vijayawada and Polavaram stations of Andhra Pradesh in India. The results indicated that SVM-QPSO is an accurate and reliable prediction tool for monthly SF.

Tehrany et al. (2015) proposed a novel ensemble method by coupling SVM and frequency ratio (FR) to produce spatial modelling in flood formation assessment in the upper catchment of the Kelantan basin in Malaysia. They applied another machine learning algorithm (decision tree (DT)) to evalulate the performance of the proposed method. Around 155 flood sites were selected from several sources over the study area. The flood sites were accidentally separated into two dataset; (115 sites) for training and the remaining (40 sites) for testing. According to results, coupling SVM and FR domestrated higher prediction accuracy than DT as the prediction rate was 85.21% and 82.00 % for

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the two methods respectively. The results demonstrated the efficiency of the proposed ensemble method in flood formation assessment.

Wei (2015) proposed a new method to predict river stages with a head prediction from 1 to 4 hr in the Tanshui River Basin, Taiwan throughout 50 historical typhoon events over 11-year period from 1996 to 2007. He employed both lazy and eager learning approaches. Two lazy learning models namely, the locally weighted regression (LWR) and the k- nearest neighbor (kNN) models and three eager learning models ANNs, SVR, and linear regression (REG) where employed in this study. According to results, in the eager learning models, ANNs and SVM produced more accurate prediction results than REG while in the lazy learning models, LWR outperformed more than kNN.