A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing

Computer Science Review, Год журнала: 2025, Номер 57, С. 100740 - 100740

Опубликована: Март 3, 2025

Язык: Английский

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Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Ноя. 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.

Язык: Английский

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Magnetorheological dampers optimization based on surrogate model and experimental verification

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 270, С. 109093 - 109093

Опубликована: Фев. 10, 2024

Язык: Английский

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

Journal of Building Pathology and Rehabilitation, Год журнала: 2024, Номер 9(1)

Опубликована: Фев. 28, 2024

Язык: Английский

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A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 135, С. 103646 - 103646

Опубликована: Май 28, 2024

Язык: Английский

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Marine diesel engine piston ring fault diagnosis based on LSTM and improved beluga whale optimization

Alexandria Engineering Journal, Год журнала: 2024, Номер 109, С. 213 - 228

Опубликована: Сен. 5, 2024

Язык: Английский

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Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region

Heliyon, Год журнала: 2023, Номер 10(1), С. e22942 - e22942

Опубликована: Ноя. 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.

Язык: Английский

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Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 11, 2024

Abstract Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting catastrophic ground failure. Accurate prediction such geotechnical parameter crucial for mitigating hazards, assessing risks, and advancing engineering. This study introduces novel predictive model combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid (ELM-DOA) compared the classical ELM, Adaptive Neuro-Fuzzy Inference System Fuzzy C-Means (ANFIS-FCM model), Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear non-linear normalization. results demonstrate normalization significantly enhances performance all models by approximately 25% Furthermore, ELM-DOA achieves most accurate predictions, exhibiting lowest root mean square error (484.286 J/m 3 ), absolute percentage (24.900%), (404.416 highest correlation determination (0.935). Additionally, Graphical User Interface (GUI) has been developed, specifically tailored model, assist engineers researchers maximizing utilization this model. GUI provides user-friendly platform easy input accessing model's enhancing its practical applicability. Overall, strongly support proposed serving as an effective tool resistance engineering, aiding predicting hazards.

Язык: Английский

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Reply on RC2

Опубликована: Янв. 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.

Язык: Английский

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Reply on RC1

Опубликована: Янв. 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.

Язык: Английский

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