Advanced Hydroinformatics

Machine Learning and Optimization for Water Resources

Inbunden, Engelska, 2024

AvGerald A. Corzo Perez,Dimitri P. Solomatine

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Applying machine learning and optimization technologies to water management problemsThe 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 hydroinformaticsAdvances in modeling hydrological systemsDifferent data analysis methods and models for forecasting water resourcesNew areas of knowledge discovery and optimization based on using machine learning techniquesCase studies from North America, South America, the Caribbean, Europe, and AsiaThe 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.

Produktinformation

Utgivningsdatum2024-01-02
Mått157 x 231 x 30 mm
Vikt844 g
FormatInbunden
SpråkEngelska
SerieSpecial Publications
Antal sidor480
FörlagJohn Wiley & Sons Inc
ISBN9781119639312

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Geovetenskap inom Naturvetenskap och teknik

List of Contributors viiPreface xi1 Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-RelatedProblems 1Gerald A. Corzo Perez and Dimitri P. SolomatinePart I Modeling Hydrological Systems2 Improving Model Identifiability by Driving Calibration With Stochastic Inputs 41Andreas Efstratiadis, Ioannis Tsoukalas, and Panagiotis Kossieris3 A Two-Stage Surrogate-Based Parameter Calibration Framework for a Complex DistributedHydrological Model 63Haiting Gu, Yue-Ping Xu, Li Liu, Di Ma, Suli Pan, and Jingkai Xie4 Fuzzy Committees of Conceptual Distributed Model 99Mostafa Farrag, Gerald A. Corzo Perez, and Dimitri P. Solomatine5 Regression-Based Machine Learning Approaches for Daily Streamflow Modeling 129Vidya S. Samadi, Sadgeh Sadeghi Tabas, Catherine A. M. E. Wilson, and Daniel R. Hitchcock6 Use of Near-Real-Time Satellite Precipitation Data and Machine Learning to Improve Extreme RunoffModeling 149Paul Muñoz, Gerald A. Corzo Perez, Dimitri P. Solomatine, Jan Feyen, and Rolando CélleriPart II Forecasting Water Resources7 Forecasting Water Levels Using Machine (Deep) Learning to Complement Numerical Modeling in theSouthern Everglades, USA 179Courtney S. Forde, Biswa Bhattacharya, Dimitri P. Solomatine, Eric D. Swain, and Nicholas G. Aumen8 Application of a Multilayer Perceptron Artificial Neural Network (MLP-ANN) in HydrologicalForecasting in El Salvador 213Jose Valles9 Noise Filter With Wavelet Analysis in Artificial Neural Networks (NOWANN) for Flow Time SeriesPrediction 241Daniel A. Vázquez, Gerald A. Corzo Perez, and Dimitri P. SolomatinePart III Knowledge Discovery and Optimization10 Application of Natural Language Processing to Identify Extreme Hydrometeorological Events inDigital News Media: Case of the Magdalena River Basin, Colombia 285Santiago Duarte, Gerald A. Corzo Perez, Germán Santos, and Dimitri P. Solomatine11 Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: ClusterSize Filter and Drought Indicator Threshold Optimization 319Vitali Diaz, Gerald A. Corzo Perez, Henny A. J. Van Lanen, and Dimitri P. Solomatine12 Deep Learning of Extreme Rainfall Patterns Using Enhanced Spatial Random Sampling With PatternRecognition 343Han Wang and Yunqing Xuan13 Teleconnection Patterns of River Water Quality Dynamics Based on Complex Network Analysis 357Jiping Jiang, Sijie Tang, Bellie Sivakumar, Tianrui Pang, Na Wu, and Yi Zheng14 Probabilistic Analysis of Flood Storage Areas Management in the Huai River Basin, China, WithRobust Optimization and Similarity-Based Selection for Real-Time Operation 373Xingyu Zhou, Andreja Jonoski, Ioana Popescu, and Dimitri P. Solomatine15 Multi-Objective Optimization of Reservoir Operation Policies Using Machine Learning Models: ACase Study of the Hatillo Reservoir in the Dominican Republic 409Carlos Tami, Gerald A. Corzo Perez, Fidel Perez, and Germain SantosIndex 447