Description

Book Synopsis

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

    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

Advanced Hydroinformatics

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A Hardback by Gerald A. Corzo Perez, Dimitri P. Solomatine

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    View other formats and editions of Advanced Hydroinformatics by Gerald A. Corzo Perez

    Publisher: John Wiley & Sons Inc
    Publication Date: 02/01/2024
    ISBN13: 9781119639312, 978-1119639312
    ISBN10: 111963931X

    Description

    Book Synopsis

    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

      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

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