AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems DOI Creative Commons

Guoping You,

Zhong Lu, Zhipeng Qiu

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 727 - 727

Published: Nov. 28, 2024

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy (AMBWO). The adaptive population learning strategy proposed improve global BWO. introduction roulette equilibrium selection allows have more reference points choose among during exploitation phase, which enhances flexibility algorithm. In addition, avoidance improves algorithm’s ability escape optima enriches quality. order validate performance AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on CEC2017 CEC2022 test sets. Statistical tests, convergence analysis, stability analysis show that AMBWO exhibits superior overall performance. Finally, applicability superiority was further verified several engineering problems.

Language: Английский

A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing DOI
Sang-Woong Lee, Amir Haider, Amir Masoud Rahmani

et al.

Computer Science Review, Journal Year: 2025, Volume and Issue: 57, P. 100740 - 100740

Published: March 3, 2025

Language: Английский

Citations

1
Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm DOI Creative Commons
Adil Masood, Mohammed Majeed Hameed, Aman Srivastava

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 29, 2023

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In context, accurate prediction of PM2.5 concentration critical for raising public awareness, allowing sensitive populations to plan ahead, providing governments with information alerts. This study applies novel hybridization extreme learning machine (ELM) snake optimization algorithm called ELM-SO model forecast concentrations. The has been developed on quality inputs meteorological parameters. Furthermore, hybrid compared individual models, Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, deep known Long Short-Term Memory networks (LSTM), forecasting results suggested exhibited highest level predictive performance among five testing value squared correlation coefficient (R2) 0.928, root mean square error 30.325 µg/m3. study's findings suggest technique valuable tool accurately concentrations could help advance field forecasting. By developing state-of-the-art pollution models incorporate ELM-SO, it may be possible understand better anticipate effects human environment.

Language: Английский

Citations

19
Magnetorheological dampers optimization based on surrogate model and experimental verification DOI
Jiahao Li, Wei Zhou, Xixiang Deng

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 270, P. 109093 - 109093

Published: Feb. 10, 2024

Language: Английский

Citations

8
Enhancing high-strength self-compacting concrete properties through Nano-silica: analysis and prediction of mechanical strengths DOI

Md. Faiz Alam,

Kumar Shubham, Sanjay Kumar

et al.

Journal of Building Pathology and Rehabilitation, Journal Year: 2024, Volume and Issue: 9(1)

Published: Feb. 28, 2024

Language: Английский

Citations

6
A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting DOI
Okan Mert Katipoğlu, Neşe Ertugay, Nehal Elshaboury

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 135, P. 103646 - 103646

Published: May 28, 2024

Language: Английский

Citations

6
Marine diesel engine piston ring fault diagnosis based on LSTM and improved beluga whale optimization DOI Creative Commons

Bingwu Gao,

Jing Xu, Huajin Zhang

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 109, P. 213 - 228

Published: Sept. 5, 2024

Language: Английский

Citations

4
Reply on RC2 DOI Creative Commons
Pablo A. Mendoza

Published: Jan. 16, 2025

Abstract. Hydrological drought is one of the main hydroclimatic hazards worldwide, affecting water availability, ecosystems and socioeconomic activities. This phenomenon commonly characterized by Standardized Streamflow Index (SSI), which widely used because its straightforward formulation calculation. Nevertheless, there limited understanding what SSI actually reveals about how climate anomalies propagate through terrestrial cycle. To find possible explanations, we implemented SUMMA hydrological model coupled with mizuRoute routing in six hydroclimatically different case study basins located on western slopes extratropical Andes, examined correlations between (computed from models for 1, 3 6-month time scales) potential explanatory variables – including precipitation simulated catchment-scale storages aggregated at scales. Additionally, analyzed impacts adopting scales propagation analyses specific events meteorological to soil moisture focus their duration intensity. The results reveal that choice scale has larger effects rainfall-dominated regimes compared snowmelt-driven basins, especially when fluxes are longer than 9 months. In all analyzed, strongest relationships (Spearman rank correlation values over 0.7) were obtained using aggregations compute 9–12 months variables, excepting aquifer storage basins. Finally, show trajectories Precipitation (SPI), Soil Moisture (SSMI) may change drastically selection scale. Overall, this highlights need caution selecting standardized indices associated scales, since event characterizations, monitoring analyses.

Language: Английский

Citations

0
Reply on RC1 DOI Creative Commons
Pablo A. Mendoza

Published: Jan. 16, 2025

Abstract. Hydrological drought is one of the main hydroclimatic hazards worldwide, affecting water availability, ecosystems and socioeconomic activities. This phenomenon commonly characterized by Standardized Streamflow Index (SSI), which widely used because its straightforward formulation calculation. Nevertheless, there limited understanding what SSI actually reveals about how climate anomalies propagate through terrestrial cycle. To find possible explanations, we implemented SUMMA hydrological model coupled with mizuRoute routing in six hydroclimatically different case study basins located on western slopes extratropical Andes, examined correlations between (computed from models for 1, 3 6-month time scales) potential explanatory variables – including precipitation simulated catchment-scale storages aggregated at scales. Additionally, analyzed impacts adopting scales propagation analyses specific events meteorological to soil moisture focus their duration intensity. The results reveal that choice scale has larger effects rainfall-dominated regimes compared snowmelt-driven basins, especially when fluxes are longer than 9 months. In all analyzed, strongest relationships (Spearman rank correlation values over 0.7) were obtained using aggregations compute 9–12 months variables, excepting aquifer storage basins. Finally, show trajectories Precipitation (SPI), Soil Moisture (SSMI) may change drastically selection scale. Overall, this highlights need caution selecting standardized indices associated scales, since event characterizations, monitoring analyses.

Language: Английский

Citations

0
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis DOI Creative Commons

Fabián Lema,

Pablo A. Mendoza, Nicolás Vasquéz

et al.

Hydrology and earth system sciences, Journal Year: 2025, Volume and Issue: 29(8), P. 1981 - 2002

Published: April 22, 2025

Abstract. Hydrological drought is one of the main hydroclimatic hazards worldwide, affecting water availability, ecosystems, and socioeconomic activities. This phenomenon commonly characterized by Standardized Streamflow Index (SSI), which widely used because its straightforward formulation calculation. Nevertheless, there limited understanding what SSI actually reveals about how climate anomalies propagate through terrestrial cycle. To find possible explanations, we implemented Structure for Unifying Multiple Modeling Alternatives (SUMMA) coupled with mizuRoute routing model in six hydroclimatically different case study basins located on western slopes extratropical Andes examined correlations between (computed from models 1-, 3-, 6-month timescales) potential explanatory variables – including precipitation simulated catchment-scale storages aggregated at timescales. Additionally, analyzed impacts adopting timescales propagation analyses specific events meteorological to soil moisture hydrological focus their duration intensity. The results reveal that choice timescale has larger effects rainfall-dominated regimes compared snowmelt-driven basins, especially when fluxes are longer than 9 months. In all analyzed, strongest relationships (Spearman rank correlation values over 0.7) were obtained using compute 9–12 months variables, excepting aquifer storage basins. Finally, show trajectories Precipitation (SPI), Soil Moisture (SSMI), may change drastically selection timescale. Overall, this highlights need caution selecting standardized indices associated timescales, since event characterizations, monitoring, analyses.

Language: Английский

Citations

0
Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region DOI Creative Commons
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 10(1), P. e22942 - e22942

Published: Nov. 28, 2023

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and economy. Precise drought forecasting trend assessment are essential for management to reduce detrimental effects of drought. However, some existing modeling techniques have limitations hinder precise forecasting, necessitating exploration suitable approaches. This study examines two models, Long Short-Term Memory (LSTM) hybrid model integrating regularized extreme learning machine Snake algorithm, forecast hydrological droughts one six months in advance. Using Multivariate Standardized Streamflow Index (MSSI) computed from 58 years streamflow data drier Malaysian stations, models were compared classical such as gradient boosting regression K-nearest validation purposes. The RELM-SO outperformed other month ahead at station S1, with lower root mean square error (RMSE = 0.1453), absolute (MAE 0.1164), higher Nash-Sutcliffe efficiency index (NSE 0.9012) Willmott (WI 0.9966). Similarly, S2, had 0.1211 MAE 0.0909), 0.8941 WI 0.9960), indicating improved accuracy comparable models. Due significant autocorrelation data, traditional statistical metrics may be inadequate selecting optimal model. Therefore, this introduced novel parameter evaluate model's effectiveness accurately capturing turning points data. Accordingly, significantly 19.32 % 21.52 when LSTM. Besides, reliability analysis showed was most accurate providing long-term forecasts. Additionally, innovative analysis, an effective method, used analyze trends. revealed October, November, December experienced occurrences than months. research advances assessment, valuable insights decision-making drought-prone regions.

Language: Английский

Citations

10