Enhancing accuracy of extreme learning machine in predicting river flow using improved reptile search algorithm

• Published in: Stochastic environmental research and risk assessment Vol. 37; no. 8; pp. 3063 - 3083
• Main Authors: Adnan, Rana Muhammad, Mostafa, Reham R., Dai, Hong-Liang, Heddam, Salim, Masood, Adil, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Artificial neural networks; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Errors; Feasibility studies; Heuristic methods; Machine learning; Math. Appl. in Environmental Science; Original Paper; Performance evaluation; Physics; Probability Theory and Stochastic Processes; Reptiles; River flow; Rivers; Search algorithms; Statistical analysis; Statistics for Engineering; Stream flow; Waste Water Technology; Water Management; Water Pollution Control; Hydroclimatic data; Extreme leaning machine; River flow modeling; Improved reptile search algorithm
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-023-02435-y

This study searches the feasibility of a new hybrid extreme leaning machine tuned with improved reptile search algorithm (ELM-IRSA), in river flow modeling. The outcomes of the new method were compared with single ELM and hybrid ELM-based methods including ELM with salp swarm algorithm (SSA), ELM with equilibrium optimizer (EO) and ELM with reptile search algorithm (RSA). The methods were evaluated using different lagged inputs of temperature, precipitation and river flow data obtained from Upper Indus Basin located in Pakistan. Models performance evaluation was based on common statistics such as root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash–Sutcliffe Efficiency. The prediction accuracy of single ELM model with respect to RMSE was improved by 2.8%, 7.7%, 15% and 20.7% using SSA, EO, RSA and IRSA metaheuristic algorithms in the test period, respectively. The ELM-IRSA model with lagged temperature and river flow inputs provided the best predictions with the RMSE improvement of 20.7%.