Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Damage assessment of structures using hybrid neuro-genetic algorithm
Applied Soft Computing
A general regression neural network
IEEE Transactions on Neural Networks
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
Applied Soft Computing
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Method of temperature measurement using image based on GRNN
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A predictive and probabilistic load-balancing algorithm for cluster-based web servers
Applied Soft Computing
A fuzzy-autoregressive model of daily river flows
Computers & Geosciences
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Advances in Artificial Neural Systems
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Alternative forms of neural networks have been applied to forecast daily river flows on a continuous basis with the purpose of understanding how recent architectures like ANFIS, GRNN and RBF compare with traditional FFBP when monsoon-fed rivers involving significant statistical bias are involved. The forecasts are made at a location called Rajghat along river Narmada in India. Division of yearly data into four seasons and development of separate networks accordingly was found to be more useful than a single network applicable for the entire year. When a variety of error criteria were viewed together the most satisfactory network for all seasons was the radial basis function, which showed better performance then FFBP, GRNN and ANFIS. The FFBP network was found to be equally acceptable as the RBF in seasons other than the monsoon. Generally the peak flows were more satisfactorily modeled by the RBF than FFBP, GRNN and ANFIS. The relatively simpler handling of data non-linearity in FFBP was more attractive than complex ones of ANFIS and GRNN. The representative statistical model, namely response surface method, yielded highly unsatisfactory results compared to any ANN model involved in this study, confirming that the complexity of ANNs is really necessary to model daily river flows.