scholarly journals Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps

2012 ◽  
Vol 15 (2) ◽  
pp. 486-502 ◽  
Author(s):  
Mukesh K. Tiwari ◽  
Ki-Young Song ◽  
Chandranath Chatterjee ◽  
Madan M. Gupta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lead Time ◽  
River Flow ◽  
Simulation Studies ◽  
River Flow Forecasting ◽  
Testing Dataset ◽  
Crucial Problem ◽  
Daily Discharge ◽  
Selection Of

Neural network (NN) models have gained much attention for river flow forecasting because of their ability to map complex non-linearities. However, the selection of appropriate length of training datasets is crucial and the uncertainty in predictions of the trained NNs with new datasets is a crucial problem. In this study, self-organising maps (SOM) are used to classify the datasets homogeneously and the performance of four types of NN models developed for daily discharge predictions – namely traditional NN, wavelet-based NN (WNN), bootstrap-based NN (BNN) and wavelet-bootstrap-based NN (WBNN) – is analysed for their applicability cluster-wise. SOM classified the training datasets into three clusters (i.e. cluster I, II and III) and the trained SOM is then used to assign testing datasets into these three clusters. Simulation studies show that the WBNN model performs better for the entire testing dataset as well as for values in clusters I and III; for cluster II the performance of BNN model is better compared with others for a 1-day lead time forecasting. Overall, it is found that the proposed methodology can enhance the accuracy and reliability of river flow forecasting.

scholarly journals Improved higher lead time river flow forecasts using sequential neural network with error updating

2014 ◽  
Vol 62 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Om Prakash ◽  
K.P. Sudheer ◽  
K. Srinivasan
Keyword(s):  
Neural Network ◽  
Lead Time ◽  
River Flow ◽  
Forecast Accuracy ◽  
Flood Management ◽  
Residual Variance ◽  
Case Examples ◽  
River Flow Forecasting ◽  
Forecast Lead Time ◽  
Artificial Neural Network Ann

Abstract This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency) was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead). The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.

A comparison between neural-network forecasting techniques-case study: river flow forecasting

1999 ◽  
Vol 10 (2) ◽  
pp. 402-409 ◽  
Author(s):  
A.F. Atiya ◽  
S.M. El-Shoura ◽  
S.I. Shaheen ◽  
M.S. El-Sherif
Keyword(s):  
Neural Network ◽  
River Flow ◽  
River Flow Forecasting ◽  
Forecasting Techniques

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

Prediction of slip in cable-drum systems using structured neural networks

2013 ◽  
Vol 228 (3) ◽  
pp. 441-456 ◽  
Author(s):  
Ergin Kilic ◽  
Melik Dolen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Size ◽  
Network Architectures ◽  
Network Development ◽  
Low Velocity ◽  
Error Band ◽  
Artificial Neural ◽  
Recurrent Type ◽  
Selection Of

This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also highlighting the corresponding challenges associated with the design phase (i.e. selection of architecture, network size, training process parameters, etc.). Prediction performances of the devised networks are evaluated rigorously via an experimental study. Finally, a structured neural network, which embodies the network with the best prediction performance, is further developed to overcome the drift observed at low velocity. The study illustrates that the resulting structured neural network could predict the slip in the mechanism within an error band of 100 µm when an absolute reference is utilized.

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