Remesan R., Mathew J. Hydrological Data Driven Modelling: A Case Study Approach

Springer, 2015. — 250 p. — (Earth Systems Data and Models 1). — ISBN: 9783319092348, 9783319092355This 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 with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.Hydroinformatics and Data-Based Modelling Issues in Hydrology
Model Data Selection and Data Pre-processing Approaches
Machine Learning and Artificial Intelligence-Based Approaches
Data Based Solar Radiation Modelling
Data Based Rainfall-Runoff Modelling
Data-Based Evapotranspiration Modeling
Application of Statistical Blockade in Hydrology