Advances in Streamflow Forecasting: From Traditional to Modern Approaches

✍ Scribed by Priyanka Sharma, Deepesh Machiwal

• Publisher: Elsevier
• Year: 2021
• Tongue: English
• Leaves: 386
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front-Matter_2021_Advances-in-Streamflow-Forecasting
Advances in Streamflow Forecasting
Copyright_2021_Advances-in-Streamflow-Forecasting
Copyright
Dedication_2021_Advances-in-Streamflow-Forecasting
Dedication
Contributors_2021_Advances-in-Streamflow-Forecasting
Contributors
About-the-editors_2021_Advances-in-Streamflow-Forecasting
About the editors
Foreword_2021_Advances-in-Streamflow-Forecasting
Foreword
Preface_2021_Advances-in-Streamflow-Forecasting
Preface
Acknowledgment_2021_Advances-in-Streamflow-Forecasting
Acknowledgment
Chapter-1---Streamflow-forecasting--overview-of-ad_2021_Advances-in-Streamfl
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
1.4.1 Time series modeling
1.4.2 Artificial neural network
1.4.3 Other AI techniques
1.4.4 Hybrid data-driven techniques
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
Chapter-2---Streamflow-forecasting-at-large-time-s_2021_Advances-in-Streamfl
2. Streamflow forecasting at large time scales using statistical models
2.1 Introduction
2.2 Overview of statistical models used in forecasting
2.2.1 Forecasting in general
2.2.1.1 ARIMA models
2.2.1.2 Exponential smoothing models
2.2.1.3 General literature
2.2.1.4 Literature in hydrology
2.3 Theory
2.3.1 ARIMA models
2.3.1.1 Definition
2.3.1.2 Forecasting with ARIMA models
2.3.2 Exponential smoothing models
2.4 Large-scale applications at two time scales
2.4.1 Application 1: multi-step ahead forecasting of 270 time series of annual streamflow
2.4.2 Application 2: multi-step ahead forecasting of 270 time series of monthly streamflow
2.5 Conclusions
Conflicts of interest
Acknowledgment
References
Chapter-3---Introduction-of-multiple-multivariate-linea_2021_Advances-in-Str
3. Introduction of multiple/multivariate linear and nonlinear time series models in forecasting streamflow process
3.1 Introduction
3.1.1 Review of MLN time series models
3.2 Methodology
3.2.1 VAR/VARX model
3.2.2 Model building procedure
3.2.3 MGARCH model
3.2.3.1 Diagonal VECH model
3.2.3.2 Testing conditional heteroscedasticity
3.2.4 Case study
3.3 Application of VAR/VARX approach
3.3.1 The VAR model
3.3.2 The VARX model
3.4 Application of MGARCH approach
3.5 Comparative evaluation of models’ performances
3.6 Conclusions
References
Chapter-4---Concepts--procedures--and-applications-of-_2021_Advances-in-Stre
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
4.2.1 Structure of artificial neural network models
4.2.1.1 Neurons and connection formula
4.2.1.2 Transfer function
4.2.1.3 Architecture of neurons
4.2.2 Network training processes
4.2.2.1 Unsupervised training method
4.2.2.2 Supervised training method
4.2.3 Artificial neural network to approximate a function
4.2.3.1 Step 1: preprocessing of data
4.2.3.1.1 Data normalization techniques
4.2.3.1.2 Principal component analysis
4.2.3.2 Step 2: choosing the best network architecture
4.2.3.3 Step 3: postprocessing of data
4.3 Types of artificial neural networks
4.3.1 Multilayer perceptron neural network
4.3.2 Static and dynamic neural network
4.3.3 Statistical neural networks
4.4 An overview of application of artificial neural network modeling in streamflow forecasting
References
Chapter-5---Application-of-different-artificial-neu_2021_Advances-in-Streamf
5. Application of different artificial neural network for streamflow forecasting
5.1 Introduction
5.2 Development of neural network technique
5.2.1 Multilayer perceptron
5.2.2 Recurrent neural network
5.2.3 Long short-term memory network
5.2.4 Gated recurrent unit
5.2.5 Convolutional neural network
5.2.6 WaveNet
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
Chapter-6---Application-of-artificial-neural-network-an_2021_Advances-in-Str
6. Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting
6.1 Introduction
6.2 Theoretical description of models
6.2.1 Artificial neural network
6.2.2 Adaptive neuro-fuzzy inference system
6.3 Application of ANN and ANFIS for prediction of peak discharge and runoff: a case study
6.3.1 Study area description
6.3.2 Methodology
6.3.2.1 Principal component analysis
6.3.2.2 Artificial neural network
6.3.2.3 Adaptive neuro-fuzzy inference system
6.3.2.4 Assessment of model performance by statistical indices
6.3.2.5 Sensitivity analysis
6.4 Results and discussion
6.4.1 Results of ANN modeling
6.4.2 Results of ANFIS modeling
6.5 Conclusions
References
Chapter-7---Genetic-programming-for-streamflow-forecast_2021_Advances-in-Str
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
7.2.1 Classical genetic programming
7.2.2 Multigene genetic programming
7.2.3 Linear genetic programming
7.2.4 Gene expression programming
7.3 A brief review of the recent studies
7.4 A case study
7.4.1 Study area and data
7.4.2 Criteria for evaluating performance of models
7.5 Results and discussion
7.6 Conclusions
References
Chapter-8---Model-tree-technique-for-streamflow-forecas_2021_Advances-in-Str
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
8.4.1 Study area
8.4.2 Methodology
8.5 Results and analysis
8.5.1 Selection of input variables
8.5.2 Model configuration
8.5.3 Model calibration and validation
8.5.4 Sensitivity analysis of model configurations towards model performance
8.5.4.1 Influence of input variable combinations
8.5.4.2 Influence of model tree variants
8.5.4.3 Influence of data proportioning
8.5.5 Selection of best-fit model for streamflow forecasting
8.6 Summary and conclusions
Acknowledgments
References
Chapter-9---Averaging-multiclimate-model-prediction-_2021_Advances-in-Stream
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
References
Chapter-10---Short-term-flood-forecasting-using-artific_2021_Advances-in-Str
10. Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree
10.1 Introduction
10.2 Theoretical background
10.2.1 Artificial neural networks
10.2.2 Extreme learning machines
10.2.3 M5 model tree
10.3 Application of ANN, ELM, and M5 model tree techniques in hourly flood forecasting: a case study
10.3.1 Study area and data
10.3.2 Methodology
10.4 Results and discussion
10.5 Conclusions
References
Chapter-11---A-new-heuristic-model-for-monthly-streamf_2021_Advances-in-Stre
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
11.2.1 Extreme learning machine model and its extensions
11.2.2 Multiple linear regression
11.3 A case study of forecasting streamflows using extreme machine learning models
11.3.1 Study area
11.4 Applications and results
11.5 Conclusions
References
Chapter-12---Hybrid-artificial-intelligence-model_2021_Advances-in-Streamflo
12. Hybrid artificial intelligence models for predicting daily runoff
12.1 Introduction
12.2 Theoretical background of MLP and SVR models
12.2.1 Support vector regression model
12.2.2 Multilayer perceptron neural network model
12.2.3 Grey wolf optimizer algorithm
12.2.4 Whale optimization algorithm
12.2.5 Hybrid MLP neural network model
12.2.6 Hybrid SVR model
12.3 Application of hybrid MLP and SVR models in runoff prediction: a case study
12.3.1 Study area and data acquisition
12.3.2 Gamma test for evaluating the sensitivity of input variables
12.3.3 Multiple linear regression
12.3.4 Performance evaluation indicators
12.4 Results and discussion
12.4.1 Identification of appropriate input variables using gamma test
12.4.2 Predicting daily runoff using hybrid AI models
12.5 Conclusions
References
Chapter-13---Flood-forecasting-and-error-simulati_2021_Advances-in-Streamflo
13. Flood forecasting and error simulation using copula entropy method
13.1 Introduction
13.2 Background
13.2.1 Artificial neural networks
13.2.2 Entropy theory
13.2.3 Copula function
13.3 Determination of ANN model inputs based on copula entropy
13.3.1 Methodology
13.3.1.1 Copula entropy theory
13.3.1.2 Partial mutual information
13.3.1.3 Input selection based on copula entropy method
13.3.2 Application of copula entropy theory in flood forecasting—a case study
13.3.2.1 Study area and data description
13.3.2.2 Flood forecasts at Three Gorges Reservoir
13.3.2.3 Flood forecasting at the outlet of Jinsha River
13.3.2.4 Performance evaluation
13.3.2.5 Results of selected model inputs
13.4 Flood forecast uncertainties
13.4.1 Distributions for fitting flood forecasting errors
13.4.2 Determination of the distributions of flood forecasting uncertainties at TGR
13.5 Flood forecast uncertainty simulation
13.5.1 Flood forecasting uncertainties simulation based on copulas
13.5.2 Flood forecasting uncertainties simulation
13.6 Conclusions
References
Appendix-1---Books-and-book-chapters-on-data-d_2021_Advances-in-Streamflow-F
1 - Books and book chapters on data-driven approaches
Appendix-2---List-of-peer-reviewed-journals-on-_2021_Advances-in-Streamflow-
2 - List of peer-reviewed journals on data-driven approaches
Appendix-3-Data-and-software_2021_Advances-in-Streamflow-Forecasting
3 - Data and software
Web resources for open data sources of streamflow
Software packages for streamflow modeling and forecasting
Index_2021_Advances-in-Streamflow-Forecasting
Index
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