Water demand forecasting using extreme learning machines
The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent tradition...
Main Authors: | Tiwari Mukesh, Adamowski Jan, Adamowski Kazimierz |
---|---|
Format: | Article |
Language: | English |
Published: |
Sciendo
2016-03-01
|
Series: | Journal of Water and Land Development |
Subjects: | |
Online Access: | http://www.degruyter.com/view/j/jwld.2016.28.issue-1/jwld-2016-0004/jwld-2016-0004.xml?format=INT |
Similar Items
-
Weekly urban water demand forecasting using a hybrid wavelet–bootstrap–artificial neural network approach
by: Adamowski Kaz, et al.
Published: (2014-10-01) -
Short-Term Water Demand Forecasting Model Combining Variational Mode Decomposition and Extreme Learning Machine
by: Youngmin Seo, et al.
Published: (2018-09-01) -
A wavelet-SARIMA-ANN hybrid model for precipitation forecasting
by: Shafaei Maryam, et al.
Published: (2016-03-01) -
Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms
by: Antón Román-Portabales, et al.
Published: (2021-07-01) -
Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction
by: Yadav Basant, et al.
Published: (2017-03-01)