Advances in Streamflow Forecasting: From Traditional to Modern Approaches – Ebook PDF Instant Delivery – ISBN(s): 9780128206737,012820673X
Product details:
- ISBN-10 : 012820673X
- ISBN-13 : 978-0128206737
- 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.
Table contents:
1. Streamflow Forecasting: Overview and Advances in Data-Driven Techniques
2. Streamflow Forecasting at Large Time Scales Using Statistical Models
3. Introduction of Multiple/Multivariate Linear and Nonlinear Time Series Models in Forecasting Streamflow Process
4. Concepts and Procedures of Artificial Neural Network Models for Streamflow Forecasting
5. Application of Different Artificial Neural Network Models in Streamflow Forecasting
6. Application of Artificial Neural Network Model and Adaptive Neuro-Fuzzy Inference System in Streamflow Forecasting
7. Genetic Programming for Streamflow Forecasting: A Concise Review of Univariate Models with a Case Study
8. Model Tree Technique for Streamflow Forecasting: A Case Study of a Sub-catchment in Tapi River Basin, India
9. Averaging Multi-climate Model Prediction of Streamflow in the Machine Learning Paradigm
10. Short-term Flood Forecasting using Artificial Neural Network, Extreme Learning Machines and M5 Tree Models
11. A New Heuristic Method for Monthly Streamflow Forecasting: Outlier-Robust Extreme Learning Machine
12. Hybrid Artificial Intelligence Models for Predicting Daily Runoff
13. Flood Forecasting and Error Simulation Using Copula and Entropy Methods
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