Journal ArticleDOI
Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach
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TLDR
The overall performance of the Levenberg–Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other Evolutionary Computation-based algorithms.About:
This article is published in Journal of Hydrology.The article was published on 2011-09-15. It has received 104 citations till now. The article focuses on the topics: Evolutionary computation & Imperialist competitive algorithm.read more
Citations
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Journal ArticleDOI
A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling
TL;DR: Comparison of methods to prevent multi-layer perceptron neural networks from overfitting of the training data in the case of daily catchment runoff modelling shows that the elaborated noise injection method may prevent overfitting slightly better than the most popular early stopping approach.
Journal ArticleDOI
ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS
Riccardo Taormina,Kwok Wing Chau +1 more
TL;DR: The Multi-Objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to return valid PIs for both rivers and for the three CL considered of 90%, 95% and 99%, indicating a viable option for straightforward design of more reliable interval-based streamflow forecasting models.
Journal ArticleDOI
Fractional-order modeling and parameter identification for lithium-ion batteries
TL;DR: In this paper, a fractional-order model (FOM) for lithium-ion batteries and its parameter identification using time-domain test data is presented, derived from a modified Randles model and taking the form of an equivalent circuit model with free non-integer differentiation orders.
Journal ArticleDOI
Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data
TL;DR: In this article, multivariate adaptive regression spline (MARS), wavelet transform artificial neural network (WA-ANN), and regular ANN (ANN) models were compared for short-term runoff forecasting in mountainous watersheds with limited data, and it was determined that the best WA-ANN and MARS models were comparable in terms of forecasting accuracy.
Ensemble flood forecasting: A review
TL;DR: The scientific drivers of this shift towards ‘ensemble flood forecasting’ and the literature evidence of the ‘added value’ of flood forecasts based on EPS are reviewed.
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
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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.
Journal ArticleDOI
Large Area Hydrologic Modeling and Assessment Part i: Model Development
TL;DR: A conceptual, continuous time model called SWAT (Soil and Water Assessment Tool) was developed to assist water resource managers in assessing the impact of management on water supplies and nonpoint source pollution in watersheds and large river basins as discussed by the authors.
A physically based, variable contributing area model of basin hydrology
Mike Kirkby,Keith Beven +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models.
Related Papers (5)
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