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Hydrological data driven modelling : a case study approach

Author: Renji Remesan; Jimson Mathew
Publisher: Cham : Springer, [2014] ©2015
Series: Earth systems data and models, v. 1.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Remesan, Renji.
Hydrological Data Driven Modelling : A Case Study Approach.
Cham : Springer International Publishing, ©2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Renji Remesan; Jimson Mathew
ISBN: 9783319092355 3319092359 3319092340 9783319092348
OCLC Number: 894554085
Description: 1 online resource (xv, 250 pages) : illustrations (some color).
Contents: Preface; Acknowledgments; Contents; 1 Introduction; Abstract; 1.1 Modelling in Hydrology; 1.1.1 Model Classification; 1.2 Stochastic Modelling Case Studies in This Book; 1.2.1 Data Driven Rainfall-Runoff Modelling; 1.2.2 Data Driven Solar Radiation Modelling; 1.2.3 Data Driven Evapotranspiration Modelling; 1.3 Why Do You Read This Book?; References; 2 Hydroinformatics and Data-Based Modelling Issues in Hydrology; Abstract; 2.1 Hydroinformatics; 2.2 Why Overfitting and How to Avoid; 2.3 Input Variable (Data) Selection; 2.4 Redundancy in Input Data and Model. 2.5 Data-Based Modeling --
Complexity, Uncertainty, and Sensitivity2.5.1 Modeling Uncertainty; 2.5.2 Model Complexity; 2.5.3 Training Data Requirements; 2.5.4 Flexibility for a Model; 2.5.5 Sensitivity of a Model; 2.5.6 Predictive Error of a Model; 2.5.7 Identifiability of a Model; 2.6 Index of Model Utility (U); 2.7 Conclusions; References; 3 Model Data Selection and Data Pre-processing Approaches; Abstract; 3.1 Implementation of Gamma Test ; 3.1.1 Background on Gamma Statistic, V-Ratio, and M-Test; 3.1.2 Assumptions in the Gamma Test; 3.1.3 Data Analysis Using Gamma Test; 3.1.4 Delta Test. 3.2 Implementation of Entropy Theory3.2.1 Multidimensional Extensions of Entropy Theory; 3.2.2 Application of Entropy Theory; 3.3 Implementation of AIC and BIC; 3.4 Implementation of Cluster Analysis; 3.4.1 Hierarchical Tree Cluster Analysis; 3.4.1.1 Single Linkage Clustering; 3.4.1.2 Complete Linkage Clustering; 3.4.1.3 Average Linkage Clustering; 3.4.1.4 Average Group Linkage Clustering (Centroid Clustering); 3.4.1.5 Ward's Linkage Clustering; 3.4.2 Partition Clustering (K-Means Clustering); 3.4.2.1 Silhouette Value; 3.5 Implementation of Principal Component Analysis. 3.6 Traditional Approaches in Data and Model Selection3.6.1 The Holdout Method; 3.6.2 Random Sub-sampling; 3.6.3 K-Fold Cross-Validation; 3.6.4 Leave-One-Out Cross-Validation; 3.6.5 Cross-Correlation Method; 3.7 Conclusions; References; 4 Machine Learning and Artificial Intelligence-Based Approaches; Abstract; 4.1 Transfer Function Models; 4.1.1 Autoregressive Model; 4.1.2 Moving Average Model; 4.1.3 Autoregressive Moving Average Model: ARMA (P, Q); 4.1.4 Autoregressive Moving Integrated Average Model: ARIMA (P, Q); 4.1.5 AutoRegressive with eXogenous Input (ARX) Model. 4.1.6 AutoRegressive Moving Average with EXogenous Input (ARMAX) Model4.2 Local Linear Regression Model; 4.3 Artificial Neural Networks Model; 4.3.1 Feed-Forward Neural Network Architecture; 4.3.2 Recurrent Artificial Neural Networks; 4.3.3 Elman Artificial Neural Networks; 4.3.4 Jordan Artificial Neural Networks; 4.3.5 Hopfield Networks; 4.3.6 Long Short Term Memory Networks; 4.4 Training Algorithms; 4.4.1 Conjugate Gradient Algorithm; 4.4.2 Broyden --
Fletcher --
Goldfarb --
Shanno Algorithm; 4.4.3 Levenberg --
Marquardt Algorithms; 4.5 Discrete Wavelet Transforms; 4.6 Hybrid Models.
Series Title: Earth systems data and models, v. 1.
Responsibility: Renji Remesan, Jimson Mathew.

Abstract:

This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on  Read more...

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