Advances in streamflow forecasting : from traditional to modern approaches /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sharma, Priyanka (Editor ), Machiwal, Deepesh (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands ; Cambridge, MA : Elsevier, [2021]
Temas:
Streamflow > Forecasting.
Cours d'eau > D�ebit > Pr�evision.
Streamflow > Forecasting
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title page
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Foreword
  • Preface
  • Acknowledgment
  • 1 Streamflow forecasting: overview of advances in data-driven techniques
  • 1.1 Introduction
  • 1.2 Measurement of streamflow and its forecasting
  • 1.3 Classification of techniques/models used for streamflow forecasting
  • 1.4 Growth of data-driven methods and their applications in streamflow forecasting
  • 1.5 Comparison of different data-driven techniques
  • 1.6 Current trends in streamflow forecasting
  • 1.7 Key challenges in forecasting of streamflows
  • 1.8 Concluding remarks
  • References
  • 2 Streamflow forecasting at large time scales using statistical models
  • 2.1 Introduction
  • 2.2 Overview of statistical models used in forecasting
  • 2.3 Theory
  • 2.4 Large-scale applications at two time scales
  • 2.5 Conclusions
  • Conflicts of interest
  • Acknowledgment
  • References
  • 3 Introduction of multiple/ multivariate linear and nonlinear time series models in forecasting streamflow process
  • 3.1 Introduction
  • 3.2 Methodology
  • 3.3 Application of VAR/VARX approach
  • 3.4 Application of MGARCH approach
  • 3.5 Comparative evaluation of models' performances
  • 3.6 Conclusions
  • References
  • 4 Concepts, procedures, and applications of artificial neural network models in streamflow forecasting
  • 4.1 Introduction
  • 4.2 Procedure for development of artificial neural network models
  • 4.3 Types of artificial neural networks
  • 4.4 An overview of application of artificial neural network modeling in streamflow forecasting
  • References
  • 5 Application of different artificial neural network for streamflow forecasting
  • 5.1 Introduction
  • 5.2 Development of neural network technique
  • 5.3 Artificial neural network in streamflow forecasting
  • 5.4 Application of ANN: a case study of the Ganges River.
  • 5.5 ANN application software and programming language
  • 5.6 Conclusions
  • 5.7 Supplementary information
  • References
  • 6 Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting
  • 6.1 Introduction
  • 6.2 Theoretical description of models
  • 6.3 Application of ANN and ANFIS for prediction of peak discharge and runoff: a case study
  • 6.4 Results and discussion
  • 6.5 Conclusions
  • References
  • 7 Genetic programming for streamflow forecasting: a concise review of univariate models with a case study
  • 7.1 Introduction
  • 7.2 Overview of genetic programming and its variants
  • 7.3 A brief review of the recent studies
  • 7.4 A case study
  • 7.5 Results and discussion
  • 7.6 Conclusions
  • References
  • 8 Model tree technique for streamflow forecasting: a case study in sub-catchment of Tapi River Basin, India
  • 8.1 Introduction
  • 8.2 Model tree
  • 8.3 Model tree applications in streamflow forecasting
  • 8.4 Application of model tree in streamflow forecasting: a case study
  • 8.5 Results and analysis
  • 8.6 Summary and conclusions
  • Acknowledgments
  • References
  • 9 Averaging multiclimate model prediction of streamflow in the machine learning paradigm
  • 9.1 Introduction
  • 9.2 Salient review on ANN and SVR modeling for streamflow forecasting
  • 9.3 Averaging streamflow predicted from multiclimate models in the neural network framework
  • 9.4 Averaging streamflow predicted by multiclimate models in the framework of support vector regression
  • 9.5 Machine learningeaveraged streamflow from multiple climate models: two case studies
  • 9.6 Conclusions
  • References
  • 10 Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree
  • 10.1 Introduction
  • 10.2 Theoretical background.
  • 10.3 Application of ANN, ELM, and M5 model tree techniques in hourly flood forecasting: a case study
  • 10.4 Results and discussion
  • 10.5 Conclusions
  • References
  • 11 A new heuristic model for monthly streamflow forecasting: outlier-robust extreme learning machine
  • 11.1 Introduction
  • 11.2 Overview of extreme learning machine and multiple linear regression
  • 11.3 A case study of forecasting streamflows using extreme machine learning models
  • 11.4 Applications and results
  • 11.5 Conclusions
  • References
  • 12 Hybrid artificial intelligence models for predicting daily runoff
  • 12.1 Introduction
  • 12.2 Theoretical background of MLP and SVR models
  • 12.3 Application of hybrid MLP and SVR models in runoff prediction: a case study
  • 12.4 Results and discussion
  • 12.5 Conclusions
  • References
  • 13 Flood forecasting and error simulation using copula entropy method
  • 13.1 Introduction
  • 13.2 Background
  • 13.3 Determination of ANN model inputs based on copula entropy
  • 13.4 Flood forecast uncertainties
  • 13.5 Flood forecast uncertainty simulation
  • 13.6 Conclusions
  • References
  • Appendix 1 Books and book chapters on data-driven approaches
  • Appendix 2 List of peer-reviewed journals on data-driven approaches
  • Appendix 3 Data and Software
  • Index.

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