Forecasting Concurrent Flows in a River System Using ANNs

Choudhury, Parthasarathi; Roy, Parthajit

doi:10.1061/(asce)he.1943-5584.0001107

Cited by 15 publications

(29 citation statements)

References 13 publications

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“…ANN can be treated as a special signal processing system with numerous interconnected layers linked by weight vectors between two neighboring layers. For instance, the authors of [14] used a particle swarm optimization model to train the parameters of ANN in stage prediction of Shing Mun River; the authors of [15] verified the feasibilities of support vector regression and ANN in river stage prediction; the authors of [16] developed a hybrid ANN method based on quantum-behaved particle swarm optimization for the daily runoff forecasting; the authors of [17] used ANN to forecast the ice conditions of the Yellow River in the inner Mongolia reach; the authors of [18] compared the performances of several AI-based methods (like ANN, and SVM) in monthly discharge predication; the authors of [19] made full use of ANN to forecast concurrent flows in a river system; and, based on ANN and SVM, the authors of [20] developed a hybrid forecasting method to effectively improve the forecast accuracy of monthly streamflow. Therefore, the above literatures indicate that ANN can provide reasonable results in water resources problems.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir

Niu

Feng

et al. 2019

Water

101

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Operation rule plays an important role in the scientific management of hydropower reservoirs, because a scientifically sound operating rule can help operators make an approximately optimal decision with limited runoff prediction information. In past decades, various effective methods have been developed by researchers all the over world, but there are few publications evaluating the performances of different methods in deriving the hydropower reservoir operation rule. To achieve satisfying scheduling process triggered by limited streamflow data, four methods are used to derive the operation rule of hydropower reservoirs, including multiple linear regression (MLR), artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). Then, the data from 1952 to 2015 in Hongjiadu reservoir of China are chosen as the survey case, and several quantitative statistical indexes are adopted to evaluate the performances of different models. The radial basis function is chosen as the kernel function of SVM, while the sigmoid function is used in the hidden layer of ELM and ANN. The simulations show that three artificial intelligence algorithms (ANN, SVM, and ELM) are able to provide better performances than the conventional MLR and scheduling graph method. Hence, for scholars in the hydropower operation field, the applications of artificial intelligence algorithms in deriving the operation rule of hydropower reservoir might be a challenge, but represents valuable research work for the future.

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Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir

Niu

Feng

et al. 2019

Water

101

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“…Flood forecasting models can be categorized as rainfall-runoff and flood routing models. In the study of Choudhury & Roy (2015), storage variables and flow rates are interlinked and governed by the following equation:…”

Section: Methodology Flood Forecasting Modelsmentioning

confidence: 99%

“…Recorded time series data for any hydrologic cycle component form the basis and are necessary for the development of a hydrologic model, and many such models have been developed and used to address many hydrological issues (Choudhury & Roy 2015). Ranging from statistical modeling to physically-based, deterministic modeling techniques, there are many approaches for the analysis of the hydrologic processes.…”

Section: Introductionmentioning

confidence: 99%

Flood forecasting and flood flow modeling in a river system using ANN

Shivam

Roy

Choudhury

et al. 2021

Water Practice and Technology

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In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following, continuity norm.

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“…For instance, a number of model structure selection methods have been derived for SISO nonlinear models in [1][2][3] and MISO processes in [4][5][6][7][8][9][10], most of which can be extended to MIMO processes. MIMO processes have been applied successfully in many different fields [11][12][13][14], but Choudhury and Roy [15] observed their limited use in dealing with hydrological problems. A MIMO process can be described as studying the interrelations of variables between inputs and outputs simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting

Tripura

Roy

Barbhuiya

2018

J. Eng. Technol. Sci.

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Simultaneous flow forecasting using multi-input multi-output (MIMO) processes is an efficient technique for accurate flow forecasting on river systems. The present study demonstrates the capability of radial basis function neural networks (RBFNN) incorporating MIMO processes in simultaneous river flow forecasting. The river system considered in the present study was the Barak river system, Assam, India. Hourly concurrent discharge data were collected from the Central Water Commission, Shillong, India from multiple sections of the Barak river system. The forecasts were tested for short-range time horizons, i.e. 1, 3, 6 and 12 hours in advance, and a comparative analysis was done using the popular Nonlinear Autoregressive with Exogenous Inputs (NARX) time series model. The result shows that MIMO-NARX provided higher prediction accuracy than MIMO-RBFNN, even at longer lead times when compared to following various statistical criterions.

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Forecasting Concurrent Flows in a River System Using ANNs

Cited by 15 publications

References 13 publications

Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir

Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir

Flood forecasting and flood flow modeling in a river system using ANN

Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting

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