Advances in Streamflow Forecasting From Traditional to Modern Approaches 1st Edition by Priyanka Sharma, Deepesh Machiwal 0128209240 9780128209240

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ISBN-10 :  0128209240 

ISBN-13 :  9780128209240

Author : Priyanka Sharma, Deepesh Machiwal 

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.

Advances in Streamflow Forecasting: From Traditional to Modern Approaches 1st Table of contents:

Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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 learning–averaged streamflow from multiple climate models: two case studies
9.6. Conclusions
Chapter 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
Chapter 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
Chapter 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
Chapter 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

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