Water resource modeling and computational technologies /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Zakwan, Mohammad (Editor ), Wahid, Abdul (Editor ), Niazkar, Majid (Editor ), Chatterjee, Uday (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, 2022.
Colección:Current directions in water scarcity research ; v. 7.
Temas:
Water-supply > Mathematical models.
Water-supply > Data processing.
Eau > Approvisionnement > Mod�eles math�ematiques.
Eau > Approvisionnement > Informatique.
Water-supply > Data processing
Water-supply > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Water Resource Modeling and Computational Technologies
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Foreword
  • Preface
  • Acknowledgments
  • Section I: Introduction
  • Chapter 1: Artificial intelligence and machine learning in water resources engineering
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. Selection of search terms
  • 2.2. Scientometric review
  • 3. Evolution of artificial intelligence and machine learning
  • 4. Results and discussion
  • 5. Conclusion
  • References
  • Section II: Application of artificial intelligence to water resources
  • Chapter 2: Demystifying artificial intelligence amidst sustainable agricultural water management
  • 1. Introduction
  • 1.1. Review objectives and chapter organization
  • 2. AI in agriculture
  • 2.1. AI in preagricultural (preparatory) activities
  • 2.1.1. Case studies: ``Agri-e-calculator and sowing app��
  • 2.2. AI during agricultural activities
  • 2.3. AI in postagricultural activities
  • 3. Current and future scope in AI for agriculture
  • 4. Challenges of AI in agriculture
  • 5. Conclusions
  • Acknowledgments
  • Conflict of interest
  • References
  • Further reading
  • Chapter 3: Bidirectional long short-term memory-based empirical wavelet transform: A new hybrid artificial
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. Study site and data used
  • 2.2. Performance assessment of the models
  • 2.3. Methodology
  • 2.3.1. Bidirectional long short term memory (BiLSTM)
  • 2.3.2. Gaussian process regression (GPR)
  • 2.3.3. Support vector regression (SVR)
  • 2.3.4. Empirical wavelet transform (EWT)
  • 3. Results and discussion
  • 4. Conclusions
  • 5. Recommendations
  • References
  • Chapter 4: Fuzzy logic modeling of groundwater potential in Marinduque, Philippines
  • 1. Introduction
  • 2. Material and methods
  • 2.1. Study site
  • 2.2. Data
  • 2.3. Groundwater potential mapping using fuzzy logic
  • 2.3.1. Identification of membership function
  • 2.3.2. Determination of aggregation function
  • 2.3.3. Calculation of the performance metrics of the fuzzy aggregation functions
  • 3. Results
  • 4. Discussion
  • 5. Conclusion
  • References
  • Chapter 5: Soft-computing approach to scour depth prediction under wall jets
  • 1. Introduction
  • 2. Materials and methods
  • 2.1. Effect of various parameters on equilibrium depth of scour
  • 2.2. Existing prediction equations for maximum scour depth
  • 3. Results and discussion
  • 3.1. Statistical error analysis
  • 3.2. Artificial neural network (ANN) model
  • 3.3. Adaptive neuro-fuzzy interference system (ANFIS) model
  • 4. Conclusions
  • References
  • Section III: Image processing applications in water resources
  • Chapter 6: Assessment of water resources using remote sensing and GIS techniques
  • 1. Introduction
  • 2. Remote sensing and GIS: Tools for sustainability of water resources

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