1st Edition
Model Induction from Data Towards the next generation of computational engines in hydraulics and hydrology
By Y.B. Dibike
Copyright 2002
156 Pages
by
CRC Press
156 Pages
by
CRC Press
156 Pages
by
CRC Press
Also available as eBook on:
There has been an explosive growth of methods in recent years for learning (or estimating dependency) from data, where data refers to known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject addressed in this thesis is model induction from data for the simulation of hydrodynamic processes in the aquatic environment. Firstly, some... Read more
Chapter 1 Introduction 1.1 Current Practices of Computational Hydraulic Modelling 1.2 Problems Associated with the Current Practice 1.3 Model Induction from Data: an Alternative Approach 1.4 Model Structure Selection 1.5 Outline of the Thesis Chapter 2 Artificial Neural Networks as Model Induction Techniques Chapter 3 Model Induction with Support Vector Machines Chapter 4 Artificial Neural Networks as Domain knowledge Encapsulators Chapter 5 Simulation of Hydrodynamic Processes Using ANNs Chapter 6 Developing Generic Hydrodynamic Models Using ANNs Chapter 7 Summary and Conclusions.
Biography
Yonas Berhan Dibike born in Addis Ababa, Ethiopia Master of Science with Distinction, IHE.
