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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      Publisher: John Wiley & Sons Inc
      Publication Date: 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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