Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties.
This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting.
This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest.
This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions.
1. Chapter 3 : Introduction of multiple/multivariate linear and nonlinear time series models in forecasting streamflow process - p. Page 78 2. Chapter 4 : Concepts, procedures, and applications of artificial neural network models in streamflow forecasting - p. Page 111 3. Chapter 5 : Application of different artificial neural network for streamflow forecasting - p. Page 137 4. Chapter 6 : Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting - p. Page 164 5. Chapter 7 : Genetic programming for streamflow forecasting: a concise review of univariate models with a case study - p. Page 189 6. Chapter 8 : Model tree technique for streamflow forecasting: a case study in sub-catchment of Tapi River Basin, India - p. Page 212 7. Chapter 9 : Averaging multiclimate model prediction of streamflow in the machine learning paradigm - p. Page 233 8. Chapter 10 : Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree - p. Page 255 9. Chapter 11 : A new heuristic model for monthly streamflow forecasting: outlier-robust extreme learning machine - p. Page 275 10. Chapter 12 : Hybrid artificial intelligence models for predicting daily runoff - p. Page 299 11. Chapter 13 : Flood forecasting and error simulation using copula entropy method - p. Page 325 12. Appendix 1 : Books and book chapters on data-driven approaches - p. Page 361 13. Appendix 2 : List of peer-reviewed journals on data-driven approaches - p. Page 361 14. Appendix 3 Data and software - p. Page 361 15. Index - p. Page 361
Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including traditional approach of statistical and stochastic time-series modelling with their recent developments, stand-alone data-driven approach such as artificial intelligence techniques, and modern hybridized approach where data-driven models are combined with preprocessing methods to improve the forecast accuracy of streamflows and to reduce the forecast uncertainties.
This book starts by providing the background information, overview, and advances made in streamflow forecasting. The overview portrays the progress made in the field of streamflow forecasting over the decades. Thereafter, chapters describe theoretical methodology of the different data-driven tools and techniques used for streamflow forecasting along with case studies from different parts of the world. Each chapter provides a flowchart explaining step-by-step methodology followed in applying the data-driven approach in streamflow forecasting.
This book addresses challenges in forecasting streamflows by abridging the gaps between theory and practice through amalgamation of theoretical descriptions of the data-driven techniques and systematic demonstration of procedures used in applying the techniques. Language of this book is kept simple to make the readers understand easily about different techniques and make them capable enough to straightforward replicate the approach in other areas of their interest.
This book will be vital for hydrologists when optimizing the water resources system, and to mitigate the impact of destructive natural disasters such as floods and droughts by implementing long-term planning (structural and nonstructural measures), and short-term emergency warning. Moreover, this book will guide the readers in choosing an appropriate technique for streamflow forecasting depending upon the given set of conditions.
1. Chapter 3 : Introduction of multiple/multivariate linear and nonlinear time series models in forecasting streamflow process - p. Page 78 2. Chapter 4 : Concepts, procedures, and applications of artificial neural network models in streamflow forecasting - p. Page 111 3. Chapter 5 : Application of different artificial neural network for streamflow forecasting - p. Page 137 4. Chapter 6 : Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting - p. Page 164 5. Chapter 7 : Genetic programming for streamflow forecasting: a concise review of univariate models with a case study - p. Page 189 6. Chapter 8 : Model tree technique for streamflow forecasting: a case study in sub-catchment of Tapi River Basin, India - p. Page 212 7. Chapter 9 : Averaging multiclimate model prediction of streamflow in the machine learning paradigm - p. Page 233 8. Chapter 10 : Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree - p. Page 255 9. Chapter 11 : A new heuristic model for monthly streamflow forecasting: outlier-robust extreme learning machine - p. Page 275 10. Chapter 12 : Hybrid artificial intelligence models for predicting daily runoff - p. Page 299 11. Chapter 13 : Flood forecasting and error simulation using copula entropy method - p. Page 325 12. Appendix 1 : Books and book chapters on data-driven approaches - p. Page 361 13. Appendix 2 : List of peer-reviewed journals on data-driven approaches - p. Page 361 14. Appendix 3 Data and software - p. Page 361 15. Index - p. Page 361
