Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 14
• Main Authors: Yaseen, Zaher Mundher, Al-Ansari, Nadhir, Faris, Hossam
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; alp Swarm Algorithm; Artificial intelligence; Artificial neural networks; Case studies; Entrapment; Forecasts and trends; Geoteknik; Heuristic methods; Hydrological Application; Hydrology; Learning Machine; Machine learning; Mathematical models; Neural networks; Optimization algorithms; Prediction models; Rain; Researchers; River flow; Soil Mechanics; SSA-ELM model; Stream flow; Tigris River; Iraq

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...