Journal ArticleDOI
Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
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TLDR
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.About:
This article is published in Journal of Hydrology.The article was published on 2000-05-08. It has received 580 citations till now. The article focuses on the topics: Overfitting & Backpropagation.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
Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir
TL;DR: Inflow of the dam reservoir in the 12 past months shows that ARIMA model had a less error compared with the ARMA model, and dynamic artificial neural network model was chosen as the best model for forecasting inflow of the Dez dam reservoir.
Journal ArticleDOI
Groundwater level forecasting using artificial neural networks
TL;DR: The different experiment results show that accurate predictions can be achieved with a standard feedforward neural network trained with the Levenberg–Marquardt algorithm providing the best results for up to 18 months forecasts.
Journal ArticleDOI
Input determination for neural network models in water resources applications. Part 1—background and methodology
TL;DR: The input determination methodology is applied to a real-world case study in order to determine suitable model inputs for forecasting salinity in the River Murray, South Australia, 14 days in advance.
Journal ArticleDOI
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
TL;DR: In this article, two nonlinear time-series models for predicting groundwater level (GWL) fluctuations using artificial neural networks (ANNs) and support vector machines (SVMs) were developed.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
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.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
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.
Related Papers (5)
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