Journal ArticleDOI
Comparison of different ANN techniques in river flow prediction
Ozgur Kisi,H. Kerem Cigizoglu +1 more
TLDR
In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria.Abstract:
Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short- and long-term forecasts of river flow events in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting. The studies in modelling the intermittent series are quite limited because of the complexity of fitting models in to these series. The available conventional models necessitate the adjustment of numerous parameters for calibration. Three different ANN techniques, namely, feed-forward back propagation (FFBP), generalized regression neural networks, and radial basis function-based neural networks (RBF) are applied to continuous and intermittent river flow data of two Turkish rivers for short-range and lo...read more
Citations
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Journal ArticleDOI
Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
TL;DR: Despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues and there is still a need for the development of robust ANN model development approaches.
Journal ArticleDOI
On comparing three artificial neural networks for wind speed forecasting
TL;DR: A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.
Journal ArticleDOI
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds.
Jan Adamowski,Karen Sun +1 more
TL;DR: The results indicate that coupled wavelet-neural network models are a promising new method of short-term flow forecasting in non-perennial rivers in semi-arid watersheds such as those found in Cyprus.
Journal ArticleDOI
Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms
TL;DR: Performance of the networks was not satisfactory because the number of neurons in the hidden layer and the roles of training, validation and testing phases were lacking flexibility and change, but improvement of network accuracy was found.
Journal ArticleDOI
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Robert J. Abrahart,François Anctil,Paulin Coulibaly,Christian W. Dawson,Nick J. Mount,Linda See,Asaad Y. Shamseldin,Dimitri Solomatine,Elena Toth,Robert L. Wilby +9 more
TL;DR: The field is now firmly established and the research community involved has much to offer hydrological science, but it will be necessary to converge on more objective and consistent protocols for selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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