Hydrological Data Driven Modelling : A Case Study Approach / Renji Remesan, Jimson Mathew

CONTENTS

1 Introduction

1.1 Modelling in Hydrology
1.2 Stochastic Modelling case studies in this book
1.3 Why do you read this book

2 Hydroinformatics and data-base Modelling issues in Hydrology

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-base modelling- Complex Uncertainty
etc

3 Model data selection and data pre-processing approaches

3.1 Implementation of Gamma test
3.2 Implementation of entropy theory
3.3 Implementation of AIC and BIC
3.4 Implementation of cluster analysis
etc

4 Machine learning and artificial intelligence-Based Approaches

4.1 Transfer function Models
4.2 Local linear regression model
4.3 Artificial Neural networks model
4.4 Training Algorithms
etc

5 Data Based solar radiation modelling

5.1 Introduction
5.2 Statistical indices for data based model comparison
5.3 Data based six-hourly solar radiation modelling
5.4 Data based daily solar radiation on beas database
etc

6 Data based rainfall-runoff modelling

6.1 Introduction
6.2 Study area: Brue catchment
6.3 Statistical Indices for comparison
6.4 Data selection approaches in data based Rainfall-Runoff modelling
6.5 Data Based Rainfall: Runoff Modelling
6.6 Conclusions

7 Data-Based Evapotranspiration modelling

7.1 Introduction
7.2 Study Area
7.3 Statistical Indices for model comparison
7.4 Modelling with traditional reference environment
7.5 Data-based evaporation modelling : Data selection approaches
etc

8 Application of statistical Blockade in hydrology

8.1 Introduction
8.2 Statistical Blockade steps
8.3 Cas study in hydrology
8.4 Conclusions

Includes Index: p.249-250