Advanced Hydroinformatics
Machine Learning and Optimization for Water Resources
1st Edition
Will be published approx. on 2. January 2024
Book
Hardback
480 pages
978-1-119-63931-2 (ISBN)
Description
Applying machine learning and optimization technologies to water management problems
The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts.
Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management.
Volume Highlights Include:
Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics
Advances in modeling hydrological systems
Different data analysis methods and models for forecasting water resources
New areas of knowledge discovery and optimization based on using machine learning techniques
Case studies from North America, South America, the Caribbean, Europe, and Asia
The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
The rapid development of machine learning brings new possibilities for hydroinformatics research and practice with its ability to handle big data sets, identify patterns and anomalies in data, and provide more accurate forecasts.
Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources presents both original research and practical examples that demonstrate how machine learning can advance data analytics, accuracy of modeling and forecasting, and knowledge discovery for better water management.
Volume Highlights Include:
Overview of the application of artificial intelligence and machine learning techniques in hydroinformatics
Advances in modeling hydrological systems
Different data analysis methods and models for forecasting water resources
New areas of knowledge discovery and optimization based on using machine learning techniques
Case studies from North America, South America, the Caribbean, Europe, and Asia
The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Dimensions
Height: 239 mm
Width: 158 mm
Thickness: 32 mm
Weight
844 gr
ISBN-13
978-1-119-63931-2 (9781119639312)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Gerald A. Corzo Perez | Dimitri P. Solomatine
Advanced Hydroinformatics
Machine Learning and Optimization for Water Resources
E-Book
12/2023
1st Edition
Wiley
€187.99
Available for download
Gerald A. Corzo Perez | Dimitri P. Solomatine
Advanced Hydroinformatics
Machine Learning and Optimization for Water Resources
E-Book
12/2023
1st Edition
Wiley
€181.99
Available for download
Persons
Gerald A. Corzo Perez, IHE Delft Institute for Water Education, The Netherlands
Dimitri P. Solomatine, IHE Delft Institute for Water Education, and Delft University of Technology, The Netherlands, and Water Problems Institute of the Russian Academy of Sciences, Moscow, Russia
Dimitri P. Solomatine, IHE Delft Institute for Water Education, and Delft University of Technology, The Netherlands, and Water Problems Institute of the Russian Academy of Sciences, Moscow, Russia
Content
List of Contributors vii
Preface xi
1 Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-RelatedProblems 1
Gerald A. Corzo Perez and Dimitri P. Solomatine
Part I Modeling Hydrological Systems
2 Improving Model Identifiability by Driving Calibration With Stochastic Inputs 41
Andreas Efstratiadis, Ioannis Tsoukalas, and Panagiotis Kossieris
3 A Two-Stage Surrogate-Based Parameter Calibration Framework for a Complex DistributedHydrological Model 63
Haiting Gu, Yue-Ping Xu, Li Liu, Di Ma, Suli Pan, and Jingkai Xie
4 Fuzzy Committees of Conceptual Distributed Model 99
Mostafa Farrag, Gerald A. Corzo Perez, and Dimitri P. Solomatine
5 Regression-Based Machine Learning Approaches for Daily Streamflow Modeling 129
Vidya S. Samadi, Sadgeh Sadeghi Tabas, Catherine A. M. E. Wilson, and Daniel R. Hitchcock
6 Use of Near-Real-Time Satellite Precipitation Data and Machine Learning to Improve Extreme RunoffModeling 149
Paul Munoz, Gerald A. Corzo Perez, Dimitri P. Solomatine, Jan Feyen, and Rolando Celleri
Part II Forecasting Water Resources
7 Forecasting Water Levels Using Machine (Deep) Learning to Complement Numerical Modeling in theSouthern Everglades, USA 179
Courtney S. Forde, Biswa Bhattacharya, Dimitri P. Solomatine, Eric D. Swain, and Nicholas G. Aumen
8 Application of a Multilayer Perceptron Artificial Neural Network (MLP-ANN) in HydrologicalForecasting in El Salvador 213
Jose Valles
9 Noise Filter With Wavelet Analysis in Artificial Neural Networks (NOWANN) for Flow Time SeriesPrediction 241
Daniel A. Vazquez, Gerald A. Corzo Perez, and Dimitri P. Solomatine
Part III Knowledge Discovery and Optimization
10 Application of Natural Language Processing to Identify Extreme Hydrometeorological Events inDigital News Media: Case of the Magdalena River Basin, Colombia 285
Santiago Duarte, Gerald A. Corzo Perez, German Santos, and Dimitri P. Solomatine
11 Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: ClusterSize Filter and Drought Indicator Threshold Optimization 319
Vitali Diaz, Gerald A. Corzo Perez, Henny A. J. Van Lanen, and Dimitri P. Solomatine
12 Deep Learning of Extreme Rainfall Patterns Using Enhanced Spatial Random Sampling With PatternRecognition 343
Han Wang and Yunqing Xuan
13 Teleconnection Patterns of River Water Quality Dynamics Based on Complex Network Analysis 357
Jiping Jiang, Sijie Tang, Bellie Sivakumar, Tianrui Pang, Na Wu, and Yi Zheng
14 Probabilistic Analysis of Flood Storage Areas Management in the Huai River Basin, China, WithRobust Optimization and Similarity-Based Selection for Real-Time Operation 373
Xingyu Zhou, Andreja Jonoski, Ioana Popescu, and Dimitri P. Solomatine
15 Multi-Objective Optimization of Reservoir Operation Policies Using Machine Learning Models: ACase Study of the Hatillo Reservoir in the Dominican Republic 409
Carlos Tami, Gerald A. Corzo Perez, Fidel Perez, and Germain Santos
Index 447
Preface xi
1 Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-RelatedProblems 1
Gerald A. Corzo Perez and Dimitri P. Solomatine
Part I Modeling Hydrological Systems
2 Improving Model Identifiability by Driving Calibration With Stochastic Inputs 41
Andreas Efstratiadis, Ioannis Tsoukalas, and Panagiotis Kossieris
3 A Two-Stage Surrogate-Based Parameter Calibration Framework for a Complex DistributedHydrological Model 63
Haiting Gu, Yue-Ping Xu, Li Liu, Di Ma, Suli Pan, and Jingkai Xie
4 Fuzzy Committees of Conceptual Distributed Model 99
Mostafa Farrag, Gerald A. Corzo Perez, and Dimitri P. Solomatine
5 Regression-Based Machine Learning Approaches for Daily Streamflow Modeling 129
Vidya S. Samadi, Sadgeh Sadeghi Tabas, Catherine A. M. E. Wilson, and Daniel R. Hitchcock
6 Use of Near-Real-Time Satellite Precipitation Data and Machine Learning to Improve Extreme RunoffModeling 149
Paul Munoz, Gerald A. Corzo Perez, Dimitri P. Solomatine, Jan Feyen, and Rolando Celleri
Part II Forecasting Water Resources
7 Forecasting Water Levels Using Machine (Deep) Learning to Complement Numerical Modeling in theSouthern Everglades, USA 179
Courtney S. Forde, Biswa Bhattacharya, Dimitri P. Solomatine, Eric D. Swain, and Nicholas G. Aumen
8 Application of a Multilayer Perceptron Artificial Neural Network (MLP-ANN) in HydrologicalForecasting in El Salvador 213
Jose Valles
9 Noise Filter With Wavelet Analysis in Artificial Neural Networks (NOWANN) for Flow Time SeriesPrediction 241
Daniel A. Vazquez, Gerald A. Corzo Perez, and Dimitri P. Solomatine
Part III Knowledge Discovery and Optimization
10 Application of Natural Language Processing to Identify Extreme Hydrometeorological Events inDigital News Media: Case of the Magdalena River Basin, Colombia 285
Santiago Duarte, Gerald A. Corzo Perez, German Santos, and Dimitri P. Solomatine
11 Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: ClusterSize Filter and Drought Indicator Threshold Optimization 319
Vitali Diaz, Gerald A. Corzo Perez, Henny A. J. Van Lanen, and Dimitri P. Solomatine
12 Deep Learning of Extreme Rainfall Patterns Using Enhanced Spatial Random Sampling With PatternRecognition 343
Han Wang and Yunqing Xuan
13 Teleconnection Patterns of River Water Quality Dynamics Based on Complex Network Analysis 357
Jiping Jiang, Sijie Tang, Bellie Sivakumar, Tianrui Pang, Na Wu, and Yi Zheng
14 Probabilistic Analysis of Flood Storage Areas Management in the Huai River Basin, China, WithRobust Optimization and Similarity-Based Selection for Real-Time Operation 373
Xingyu Zhou, Andreja Jonoski, Ioana Popescu, and Dimitri P. Solomatine
15 Multi-Objective Optimization of Reservoir Operation Policies Using Machine Learning Models: ACase Study of the Hatillo Reservoir in the Dominican Republic 409
Carlos Tami, Gerald A. Corzo Perez, Fidel Perez, and Germain Santos
Index 447