Improved random vector functional link network with an enhanced remora optimization algorithm for predicting monthly streamflow

[Display omitted] •Enhanced Remora Optimization Algorithm (EROA) optimizes RVFL for monthly streamflow prediction.•EROA's accuracy is compared with RVFL tuned by GTO, WOA, ROA, and standalone RVFL.•Several input combinations were assessed based on lagged values of streamflow, temperature and ra...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) Vol. 650; p. 132496
Main Authors: Adnan, Rana Muhammad, Mostafa, Reham R., Wang, Mo, Parmar, Kulwinder Singh, Kisi, Ozgur, Zounemat-Kermani, Mohammad
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2025
Subjects:
Enhanced remora optimization algorithm
Gorilla troops optimizer
Hydroclimatic data
Random vector functional link network
Streamflow prediction
Whale optimization algorithm
Streamflow prediction
Enhanced remora optimization algorithm
Hydroclimatic data
Gorilla troops optimizer
Whale optimization algorithm
Random vector functional link network
ISSN:0022-1694
Online Access:Get full text
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Summary:[Display omitted] •Enhanced Remora Optimization Algorithm (EROA) optimizes RVFL for monthly streamflow prediction.•EROA's accuracy is compared with RVFL tuned by GTO, WOA, ROA, and standalone RVFL.•Several input combinations were assessed based on lagged values of streamflow, temperature and rainfall data.•RVFL-EROA model outperformed the other benchmark models in predicting monthly streamflow.•The proposed model was also found to be more accurate in estimating peak streamflow values compared to other models. Precise and robust streamflow estimation is crucial for effective water resource management, particularly in mitigating extreme climatic events such as droughts and floods. This study introduces an innovative integration of the Random Vector Functional Link (RVFL) network with an Enhanced Remora Optimization Algorithm (EROA), specifically designed for monthly streamflow prediction. The RVFL-EROA is compared against standalone RVFL and RVFL models optimized using the Gorilla Troops Optimizer (GTO), Whale Optimization Algorithm (WOA), and the original Remora Optimization Algorithm (ROA). Performance is evaluated using statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash-Sutcliffe Efficiency (NSE). The methodology is tested on streamflow time series data from the Kunhar River Basin in Pakistan, with input variables derived from antecedent streamflow, air temperature, and rainfall. Results indicate that temperature and streamflow-based inputs yielded higher accuracy compared to rainfall inputs. The RVFL-EROA outperformed other models, achieving improvements in mean RMSE, MAE, R2, and NSE by 8.63–1.77%, 12.08–1.58%, 16.88–3.33%, and 19.03–3.05%, respectively. Moreover, the RVFL-EROA demonstrated superior performance in estimating peak streamflow values, which is critical for flood management. These findings highlight the potential of temperature-based inputs and RVFL-EROA models for streamflow prediction in data-scarce regions, particularly in developing countries. The proposed approach offers a reliable solution for enhancing hydrological forecasting and supports efficient water resource planning.
ISSN:0022-1694
DOI:10.1016/j.jhydrol.2024.132496