On the application of artificial neural networks and genetic algorithms in hydro-meteorological modelling
- Fernando, Dweepika Achela Kumarihamy.
DOIAbstract
published_or_final_versionCivil and Structural EngineeringDoctoralDoctor of Philosoph
published_or_final_versionCivil and Structural EngineeringDoctoralDoctor of Philosoph
Includes bibliographical references.23rd annual AGU hydrology days was held on March 31 - April 2, 2...
Water level forecasting using records of past time series is of importance in water resources engine...
This introductory chapter establishes the theoretical and contextual background for the application ...
This 2005 version has been superseded by the 2017 edition of the book, which includes exercises with...
In this chapter, two of the most important artificial intelligence techniques are presented together...
A daily rainfall-runoff model has been improved by the integration of artificial neural network (ANN...
Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publicatio...
The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genet...
Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy s...
Abstract-Weather is certainly the most important factor over which man has no control, and hence it ...
International audienceArtificial Neural Networks (ANNs) have proved to be good modelling tools in hy...
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of...
Summarization: A Differential Evolution (DE) algorithm is combined with an Artificial Neural Network...
Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in w...
The use of rainfall-runoff models in the decision making process of water resources planning and man...
Includes bibliographical references.23rd annual AGU hydrology days was held on March 31 - April 2, 2...
Water level forecasting using records of past time series is of importance in water resources engine...
This introductory chapter establishes the theoretical and contextual background for the application ...
This 2005 version has been superseded by the 2017 edition of the book, which includes exercises with...
In this chapter, two of the most important artificial intelligence techniques are presented together...
A daily rainfall-runoff model has been improved by the integration of artificial neural network (ANN...
Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publicatio...
The major objective of this chapter is to illustrate how artificial neural networks (ANNs) and genet...
Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy s...
Abstract-Weather is certainly the most important factor over which man has no control, and hence it ...
International audienceArtificial Neural Networks (ANNs) have proved to be good modelling tools in hy...
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of...
Summarization: A Differential Evolution (DE) algorithm is combined with an Artificial Neural Network...
Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in w...
The use of rainfall-runoff models in the decision making process of water resources planning and man...
Includes bibliographical references.23rd annual AGU hydrology days was held on March 31 - April 2, 2...
Water level forecasting using records of past time series is of importance in water resources engine...
This introductory chapter establishes the theoretical and contextual background for the application ...
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