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SCIDIR_on1257480214 |
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20231120010549.0 |
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|a 1257667092
|a 1287883163
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|a 9780128209240
|q (electronic bk.)
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|a 0128209240
|q (electronic bk.)
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|z 9780128206737
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|z 012820673X
|q (paperback)
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|a (OCoLC)1257480214
|z (OCoLC)1257667092
|z (OCoLC)1287883163
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|a GB1207
|b .A38 2021
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| 082 |
0 |
4 |
|a 551.48/3
|2 23
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| 245 |
0 |
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|a Advances in streamflow forecasting :
|b from traditional to modern approaches /
|c edited by Priyanka Sharma, Deepesh Machiwal.
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| 264 |
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1 |
|a Amsterdam, Netherlands ;
|a Cambridge, MA :
|b Elsevier,
|c [2021]
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| 300 |
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|a 1 online resource (xxii, 381 pages) :
|b illustrations (some colour)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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0 |
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|a Print version record.
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|a Cover -- Title page -- Copyright -- 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 -- 1.5 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 -- 2.3 Theory -- 2.4 Large-scale applications at two time scales -- 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 -- 3.2 Methodology -- 3.3 Application of VAR/VARX approach -- 3.4 Application of MGARCH approach -- 3.5 Comparative evaluation of models' performances -- 3.6 Conclusions -- 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 -- 4.3 Types of artificial 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 -- 5.3 Artificial neural network in streamflow forecasting -- 5.4 Application of ANN: a case study of the Ganges River.
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| 505 |
8 |
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|a 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 -- 6.3 Application of ANN and ANFIS for prediction of peak discharge and runoff: a case study -- 6.4 Results and discussion -- 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 -- 7.3 A brief review of the recent studies -- 7.4 A case study -- 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 -- 8.5 Results and analysis -- 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 learningeaveraged 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.
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| 505 |
8 |
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|a 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 -- 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 -- 11.3 A case study of forecasting streamflows using extreme machine learning models -- 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 -- 12.3 Application of hybrid MLP and SVR models in runoff prediction: a case study -- 12.4 Results and discussion -- 12.5 Conclusions -- References -- 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 -- References -- Appendix 1 Books and book chapters on data-driven approaches -- Appendix 2 List of peer-reviewed journals on data-driven approaches -- Appendix 3 Data and Software -- Index.
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| 650 |
|
0 |
|a Streamflow
|x Forecasting.
|
| 650 |
|
6 |
|a Cours d'eau
|0 (CaQQLa)201-0069503
|x D�ebit
|0 (CaQQLa)201-0069503
|x Pr�evision.
|0 (CaQQLa)201-0380155
|
| 650 |
|
7 |
|a Streamflow
|x Forecasting
|2 fast
|0 (OCoLC)fst01134624
|
| 700 |
1 |
|
|a Sharma, Priyanka,
|e editor.
|
| 700 |
1 |
|
|a Machiwal, Deepesh,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|t Advances in streamflow forecasting
|z 9780128206737
|w (OCoLC)1220995133
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128206737
|z Texto completo
|
