{"product_id":"advanced-hydroinformatics-isbn-9781119639312","title":"Advanced Hydroinformatics","description":"\u003cp\u003e\u003cb\u003eApplying machine learning and optimization technologies to water management problems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAdvanced Hydroinformatics: Machine Learning and Optimization for Water Resources\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVolume Highlights Include:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOverview of the application of artificial intelligence and machine learning techniques in hydroinformatics\u003c\/li\u003e \u003cli\u003eAdvances in modeling hydrological systems\u003c\/li\u003e \u003cli\u003eDifferent data analysis methods and models for forecasting water resources\u003c\/li\u003e \u003cli\u003eNew areas of knowledge discovery and optimization based on using machine learning techniques\u003c\/li\u003e \u003cli\u003eCase studies from North America, South America, the Caribbean, Europe, and Asia\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eThe 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.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Contributors vii\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e1 Hydroinformatics and Applications of Artificial Intelligence and Machine Learning in Water-RelatedProblems 1\u003cbr\u003e \u003ci\u003eGerald A. Corzo Perez and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Modeling Hydrological Systems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2 Improving Model Identifiability by Driving Calibration With Stochastic Inputs 41\u003cbr\u003e \u003ci\u003eAndreas Efstratiadis, Ioannis Tsoukalas, and Panagiotis Kossieris\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 A Two-Stage Surrogate-Based Parameter Calibration Framework for a Complex DistributedHydrological Model 63\u003cbr\u003e \u003ci\u003eHaiting Gu, Yue-Ping Xu, Li Liu, Di Ma, Suli Pan, and Jingkai Xie\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Fuzzy Committees of Conceptual Distributed Model 99\u003cbr\u003e \u003ci\u003eMostafa Farrag, Gerald A. Corzo Perez, and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Regression-Based Machine Learning Approaches for Daily Streamflow Modeling 129\u003cbr\u003e \u003ci\u003eVidya S. Samadi, Sadgeh Sadeghi Tabas, Catherine A. M. E. Wilson, and Daniel R. Hitchcock\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Use of Near-Real-Time Satellite Precipitation Data and Machine Learning to Improve Extreme RunoffModeling 149\u003cbr\u003e \u003ci\u003ePaul Muñoz, Gerald A. Corzo Perez, Dimitri P. Solomatine, Jan Feyen, and Rolando Célleri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Forecasting Water Resources\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7 Forecasting Water Levels Using Machine (Deep) Learning to Complement Numerical Modeling in theSouthern Everglades, USA 179\u003cbr\u003e \u003ci\u003eCourtney S. Forde, Biswa Bhattacharya, Dimitri P. Solomatine, Eric D. Swain, and Nicholas G. Aumen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Application of a Multilayer Perceptron Artificial Neural Network (MLP-ANN) in HydrologicalForecasting in El Salvador 213\u003cbr\u003e \u003ci\u003eJose Valles\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Noise Filter With Wavelet Analysis in Artificial Neural Networks (NOWANN) for Flow Time SeriesPrediction 241\u003cbr\u003e \u003ci\u003eDaniel A. Vázquez, Gerald A. Corzo Perez, and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Knowledge Discovery and Optimization\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10 Application of Natural Language Processing to Identify Extreme Hydrometeorological Events inDigital News Media: Case of the Magdalena River Basin, Colombia 285\u003cbr\u003e \u003ci\u003eSantiago Duarte, Gerald A. Corzo Perez, Germán Santos, and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics: ClusterSize Filter and Drought Indicator Threshold Optimization 319\u003cbr\u003e \u003ci\u003eVitali Diaz, Gerald A. Corzo Perez, Henny A. J. Van Lanen, and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Deep Learning of Extreme Rainfall Patterns Using Enhanced Spatial Random Sampling With PatternRecognition 343\u003cbr\u003e \u003ci\u003eHan Wang and Yunqing Xuan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Teleconnection Patterns of River Water Quality Dynamics Based on Complex Network Analysis 357\u003cbr\u003e \u003ci\u003eJiping Jiang, Sijie Tang, Bellie Sivakumar, Tianrui Pang, Na Wu, and Yi Zheng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Probabilistic Analysis of Flood Storage Areas Management in the Huai River Basin, China, WithRobust Optimization and Similarity-Based Selection for Real-Time Operation 373\u003cbr\u003e \u003ci\u003eXingyu Zhou, Andreja Jonoski, Ioana Popescu, and Dimitri P. Solomatine\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Multi-Objective Optimization of Reservoir Operation Policies Using Machine Learning Models: ACase Study of the Hatillo Reservoir in the Dominican Republic 409\u003cbr\u003e \u003ci\u003eCarlos Tami, Gerald A. Corzo Perez, Fidel Perez, and Germain Santos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 447\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGerald A. Corzo Perez,\u003c\/b\u003e IHE Delft Institute for Water Education, The Netherlands \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDimitri P. Solomatine,\u003c\/b\u003e 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   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAdvanced Hydroinformatics\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eMachine Learning and Optimization for Water Resources \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAdvanced Hydroinformatics: Machine Learning and Optimization for Water Resources\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVolume Highlights Include:\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eOverview of the application of artificial intelligence and machine learning techniques in hydroinformatics\u003c\/li\u003e \u003cli\u003eAdvances in modeling hydrological systems\u003c\/li\u003e \u003cli\u003eDifferent data analysis methods and models for forecasting water resources\u003c\/li\u003e \u003cli\u003eNew areas of knowledge discovery and optimization based on using machine learning techniques\u003c\/li\u003e \u003cli\u003eCase studies from North America, South America, the Caribbean, Europe, and Asia\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eThe 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.\u003c\/i\u003e\u003c\/p\u003e","brand":"American Geophysical Union","offers":[{"title":"Default Title","offer_id":47988667285733,"sku":"NP9781119639312","price":210.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119639312.jpg?v=1761781185","url":"https:\/\/k12savings.com\/es\/products\/advanced-hydroinformatics-isbn-9781119639312","provider":"K12savings","version":"1.0","type":"link"}