| Description |
1 online resource (xxii, 381 pages) : illustrations (some colour) |
| Contents |
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 |
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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 |
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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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Streamflow -- Forecasting
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Streamflow -- Forecasting
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| Form |
Electronic book
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| Author |
Sharma, Priyanka, editor
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Machiwal, Deepesh, editor
|
| ISBN |
9780128209240 |
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0128209240 |
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