Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis
Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed...
Main Authors: | Hafiza Mamona Nazir, Ijaz Hussain, Muhammad Faisal, Alaa Mohamd Shoukry, Showkat Gani, Ishfaq Ahmad |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2019-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2019/2782715 |
Similar Items
-
Multidecompositions of crowns
by: Chun-Ning Hou, et al.
Published: (2011) -
An improved framework to predict river flow time series data
by: Hafiza Mamona Nazir, et al.
Published: (2019-07-01) -
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
by: Zulifqar Ali, et al.
Published: (2017-01-01) -
Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model
by: Hafiza Mamona Nazir, et al.
Published: (2019-12-01) -
Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model
by: Hafiza Mamona Nazir, et al.
Published: (2020-11-01)