Journal ArticleDOI
Neural-Network Models of Rainfall-Runoff Process
Jason Smith,Robert N. Eli +1 more
TLDR
In this article, a back-propagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern, and the neural network was trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.Abstract:
Spatially distributed rainfall patterns can now be detected using a variety of remote–sensing techniques ranging from weather radar to various satellite–based sensors. Conversion of the remote–sensed signal into rainfall rates, and hence into runoff for a given river basin, is a complex and difficult process using traditional approaches. Neural–network models hold the possibility of circumventing these difficulties by training the network to map rainfall patterns into various measures of runoff that may be of interest. To investigate the potential of this approach, a very simple 5 × 5 grid cell synthetic watershed is used to generate runoff from stochastically generated rainfall patterns. A back–propagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern. Additionally, the neural network is trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.read more
Citations
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Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Journal ArticleDOI
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
TL;DR: In this article, the authors investigate the role of artificial neural networks (ANNs) in hydrology and show that ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic.
Journal ArticleDOI
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
TL;DR: The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.
Journal ArticleDOI
Hydrological modelling using artificial neural networks
TL;DR: A template is proposed in order to assist the construction of future ANN rainfall-runoff models and it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
Journal ArticleDOI
Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
TL;DR: The results show that the proposed early stopped training approach (STA) is effective for improving prediction accuracy and offers an alternative when dynamic adaptive forecasting is desired.
References
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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.
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.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Book
Introduction to artificial neural systems
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Journal ArticleDOI
A simple procedure for pruning back-propagation trained neural networks
TL;DR: Shadow arrays are introduced which keep track of the incremental changes to the synaptic weights during a single pass of back-propagating learning and are ordered by decreasing sensitivity numbers so that the network can be efficiently pruned by discarding the last items of the sorted list.