Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application
The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excel...
Saved in:
Main Authors: | Zaher Mundher Yaseen, Hossam Faris, Nadhir Al-Ansari |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2020/8206245 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model
by: Mariamme Mohammed, et al.
Published: (2020-01-01) -
Assessing the Uncertainty Associated with Flood Features due to Variability of Rainfall and Hydrological Parameters
by: Ahmad Sharafati, et al.
Published: (2020-01-01) -
Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management
by: Anjali Agrawal, et al.
Published: (2022-01-01) -
Bayesian network structure learning algorithm based on hybrid binary salp swarm-differential evolution algorithm
by: Bin LIU, et al.
Published: (2019-07-01) -
BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches
by: Pinakshi Panda, et al.
Published: (2025-01-01)