Stage and Discharge Forecasting by SVM and ANN Techniques

Aggarwal, Shubhani; Goel, Arun; Singh, Vijay P.

doi:10.1007/s11269-012-0098-x

Cited by 43 publications

(17 citation statements)

References 23 publications

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“…Looped rating curve was successfully traced by SVM when the data set was divided into rising and falling stage data set. Aggarwal et al (2012) again employed SVM, ANN and persistence model for forecasting stage-discharge for Tikarapara site on Mahanadi for a period of 1-day, 7-day and 1-year. SVM provided the best forecast for stage as well as the discharge.…”

Section: Materials and Methodologymentioning

confidence: 99%

Review of Application of Data Driven Techniques in Discharge Rating Curve

Zakwan¹,

Muzzammil²,

Alam³

2017

Aquademia: Water, Environment and Technology

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Establishing a reliable rating curve is an integral part of water resource engineering. The present paper is a review of data driven techniques used for modelling the stage discharge relationship over the last two decades. Over the year's several data driven techniques such as ANN, SVM, MT, fuzzy logic and GA have been applied by researchers in quest of arriving at a reliable technique to model the stage discharge relationship. Although, during this period several techniques have been used to establish the stage discharge relationship but the application of modern optimization tools such as ACO, PSO or PS has remained limited in this field. Therefore, application of modern optimization techniques and their comparison with existing literature would be appreciated in near future.

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Section: Materials and Methodologymentioning

confidence: 99%

Review of Application of Data Driven Techniques in Discharge Rating Curve

Zakwan¹,

Muzzammil²,

Alam³

2017

Aquademia: Water, Environment and Technology

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“…Unfortunately, the functional relationship between stage and discharge is complex, time-varying, and cannot always captured by simple rating curve, even with the help of traditional modeling techniques such as polynomial regression or autoregressive integrated moving average ARIMA technique (Bhattacharya and Solomatine 2000). Many research attempts to establish this relation via data-driven techniques such as artificial neural networks ANNs (Tawfik et al 1997;Bhattacharya and Solomatine 2000;Sudheer and Jain 2003;Bisht et al 2010), decision trees (Bhattacharya and Solomatine 2003;Ghimire and Reddy 2010;Ajmera and Goyal 2012), support vector machine (Aggarwal et al 2012), wavelet-regression model (Kişi 2011), Takagi-Sugeno fuzzy inference system (Lohani et al 2006), and evolutionary-based data-driven models (Ghimire and Reddy 2010;Azamathulla et al 2011). The results approve that these techniques are very efficient and reliable.…”

Section: Introductionmentioning

confidence: 96%

Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study

Al-Abadi

2014

Appl Water Sci

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The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination (R 2 ) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2 , the M5 method has the lowest RMSE (8.10 m 3 /s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.

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“…The appropriate combination of river stage and discharge of past time steps is used as inputs for modeling river flow in a time series perspective. The techniques such as artificial neural networks (ANNs), support vector machine (SVM), M5 model trees, fuzzy inference system /adaptive neuro-fuzzy infernece system (FIS/ANFIS) are used in popular tools for discharge predictions considering lagged stage and/or discharge values (Deka and Chandramouli 2003;Sudheer and Jain 2003;Sivapragasam and Muttil 2005;Bhattacharya and Solomatine 2005;Kisi and Cobaner 2009;Aggarwal et al 2012; Al-Abadi 2016, to name a few). Later on, the hybrid soft computing models based on wavelet transforms became popular for discharge prediction (Wei et al 2012;Tiwari and Chatterjee 2011;Shiri and Kisi 2010;Seghal et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Developing stage–discharge relationships using multivariate empirical mode decomposition-based hybrid modeling

Adarsh¹,

John²,

Anagha³

et al. 2018

Appl Water Sci

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This paper proposes an alternative method for modeling stage-discharge relationships by accounting significant information from different process scales employing the multivariate empirical mode decomposition (MEMD). First, the multivariate dataset comprising the discharge of current time step and appropriate lagged inputs of stage and discharge are decomposed using MEMD. Then, genetic programming (GP)-based models are developed for each sub-series to predict discharge of current time step, considering the sub-series of predictors at the corresponding timescale as inputs. Finally, the predicted sub-series are recombined to obtain the discharge of current time step. The method is applied for the prediction of the daily discharge from Pattazhy station of Kallada River in the state of Kerala, India. Statistical evaluation based on different performance criteria revealed the substantial improvement in performance of the proposed MEMD-based hybrid model over the popular nonlinear models like GP and M5 model trees. The application of the method for three more stations Ayilam, Thumpamon and Malakkara falling in different rivers in southern Kerala confirmed the robustness of the approach even in handing zero values and extreme flows in addition to the overall improvement in predictability of daily streamflow. The superiority of the proposed method is attributed to its capability in handling multiple causative inputs and capturing significant information from different timescales common to these input variables.

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Stage and Discharge Forecasting by SVM and ANN Techniques

Cited by 43 publications

References 23 publications

Review of Application of Data Driven Techniques in Discharge Rating Curve

Review of Application of Data Driven Techniques in Discharge Rating Curve

Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study

Developing stage–discharge relationships using multivariate empirical mode decomposition-based hybrid modeling

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