Advances in Streamflow Forecasting: From Traditional to Modern Approaches

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
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