Journal ArticleDOI
Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.
TLDR
Results demonstrate that NF model presents better performance in SSC prediction in compression to other models; while ANN and NF models depict better results than MLR and SRC methods.About:
This article is published in Science of The Total Environment.The article was published on 2009-08-15. It has received 216 citations till now.read more
Citations
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Journal ArticleDOI
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review
TL;DR: The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle.
Journal ArticleDOI
Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process
TL;DR: Two hybrid AI-based models which are reliable in capturing the periodicity features of the process are introduced for watershed rainfall–runoff modeling and show that the second model is relatively more appropriate because it uses the multi-scale time series of rainfall and runoff data in the ANFIS input layer.
Journal ArticleDOI
A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States
TL;DR: Results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.
Journal ArticleDOI
Daily suspended sediment load prediction using artificial neural networks and support vector machines
TL;DR: The obtained results show that ANN models and nu-SVR model using Gamma Test for input selection has better performance than regression combination, and M-test can be used as a new method to determine the number of required data for network training for creating a smooth model by nu- SVR and ANN models.
Journal ArticleDOI
Advances in ungauged streamflow prediction using artificial neural networks
TL;DR: In this article, the authors developed and tested two artificial neural networks (ANNs) to forecast streamflow in ungauged basins using time-lagged records of precipitation and temperature.
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.
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
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Book
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.