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.
Table des matières
List of Contributors vii
Preface xi
1 Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-Related Problems 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 Distributed Hydrological 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 Runoff Modeling 149
Paul Muñoz, Gerald A. Corzo Perez, Dimitri P. Solomatine, Jan Feyen, and Rolando Célleri
Part II Forecasting Water Resources
7 Forecasting Water Levels Using Machine (Deep) Learning to Complement Numerical Modeling in the Southern 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 Hydrological Forecasting in El Salvador 213
Jose Valles
9 Noise Filter With Wavelet Analysis in Artificial Neural Networks (NOWANN) for Flow Time Series Prediction 241
Daniel A. Vázquez, 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 in Digital News Media: Case of the Magdalena River Basin, Colombia 285
Santiago Duarte, Gerald A. Corzo Perez, Germán Santos, and Dimitri P. Solomatine
11 Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: Cluster Size 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 Pattern Recognition 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, With Robust 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
A propos de l’auteur
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
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