Hydrological Data Driven Modelling : A Case Study Approach / by Renji Remesan, Jimson Mathew.
Series: Earth systems Data Models 1 | Earth Systems Data and Models ; 1Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: 1 online resource (XV, 250 pages 172 illustrations, 59 illustrations in color.)Content type:- text
- computer
- online resource
- 9783319350288
- 550 23 REM
Item type | Current library | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
Book Closed Access | Natural Resources and Environmental Sciences Library | 551.4REM (Browse shelf(Opens below)) | 1 | Available | 0018820 |
Table of contents
1. Introduction 1-17
2. Hydro informatics and Data-Based Modelling Issues in Hydrology 19-39
3. Model Data Selection and Data Pre-processing Approaches 41-67
4. Machine Learning and Artificial Intelligence-Based Approaches 71-105
5. Data Based Solar Radiation Modelling 111-149
6. Data Based Rainfall-Runoff Modelling 151-181
7. Data-Based Evapotranspiration Modeling 183-229
8. Application of Statistical Blockade in Hydrology 231-246
Index 249
Introduction -- Hydroinformatics and Data based Modelling Issues in Hydrology -- 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 Modelling -- Application of Statistical Blockade in Hydrology.
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 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.
Description based on publisher-supplied MARC data.
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