Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources(Special Publications)

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 of Contents:
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 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 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. 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 inDigital 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: 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

About the Author :
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