Advances in Streamflow Forecasting From Traditional to Modern Approaches
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 a...
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Elsevier
2021
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| ISBN: | 9780128206737, 012820673X |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Sharma, Priyanka Machiwal, Deepesh |
| Author_xml | – sequence: 1 fullname: Sharma, Priyanka – sequence: 2 fullname: Machiwal, Deepesh |
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| DOI | 10.1016/B978-0-12-820673-7.01001-5 |
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| Discipline | Geography Geology |
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| Snippet | Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow forecasting including... |
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| SubjectTerms | Streamflow Streamflow-Forecasting |
| Subtitle | From Traditional to Modern Approaches |
| TableOfContents | Front Cover -- Advances in Streamflow Forecasting -- Advances in Streamflow Forecasting: From Traditional to Modern Approaches -- Copyright -- Dedication -- 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 -- 1.4.1 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 -- 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 -- 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 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 -- 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 -- 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 -- 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 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 -- 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 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 -- 6.2.1 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 -- 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 -- 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 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 -- 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 -- 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 |
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