Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures DOI Creative Commons
Mohammed Majeed Hameed, Adil Masood,

Aman Srivast

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Фев. 19, 2024

Abstract Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting catastrophic ground failure. Accurate prediction such geotechnical parameters crucial for mitigating hazards, assessing risks, and advancing engineering. This study introduces novel predictive model combines the Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid (ELM-DOA) compared 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/m3), absolute percentage (24.9%), (404.416 highest correlation determination (0.935). Additionally, Graphical User Interface (GUI) has been developed, specifically tailored model, aid engineers researchers effectively utilizing 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

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

и другие.

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

Процитировано

21
Magnetorheological dampers optimization based on surrogate model and experimental verification DOI
Jiahao Li, Wei Zhou, Xixiang Deng

и другие.

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

Опубликована: Фев. 10, 2024

Язык: Английский

Процитировано

9
Enhancing high-strength self-compacting concrete properties through Nano-silica: analysis and prediction of mechanical strengths DOI

Md. Faiz Alam,

Kumar Shubham, Sanjay Kumar

и другие.

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

Опубликована: Фев. 28, 2024

Язык: Английский

Процитировано

7
A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting DOI
Okan Mert Katipoğlu, Neşe Ertugay, Nehal Elshaboury

и другие.

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

Опубликована: Май 28, 2024

Язык: Английский

Процитировано

7
A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing DOI
Sang-Woong Lee, Amir Haider, Amir Masoud Rahmani

и другие.

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

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

1
Marine diesel engine piston ring fault diagnosis based on LSTM and improved beluga whale optimization DOI Creative Commons

Bingwu Gao,

Jing Xu, Huajin Zhang

и другие.

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

Опубликована: Сен. 5, 2024

Язык: Английский

Процитировано

5
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

и другие.

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

Процитировано

11
Reply on RC2 DOI Creative Commons
Pablo A. Mendoza

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

Процитировано

0
Reply on RC1 DOI Creative Commons
Pablo A. Mendoza

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

Процитировано

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

и другие.

Hydrology and earth system sciences, Год журнала: 2025, Номер 29(8), С. 1981 - 2002

Опубликована: Апрель 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.

Язык: Английский

Процитировано

0