Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Hameed, Mohammed Majeed; Mohd Razali, Siti Fatin; Wan Mohtar, Wan Hanna Melini; Yaseen, Zaher Mundher

doi:10.1007/s00477-023-02548-4

Cited by 9 publications

(3 citation statements)

References 97 publications

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“…It is a feedforward neural network with a simple structure and fast processing operation 35 . During training, ELM uses many input neurons randomly selected from the available input features 45 . These input neurons are then connected to the hidden layer through randomly generated weights (Fig.…”

Section: Methodsmentioning

confidence: 99%

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

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

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

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Section: Methodsmentioning

confidence: 99%

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

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…This hybrid plot displays multiple layers and includes a mean marker, akin to the traditional violin and box plots (see Fig. 7 a) 92 . Moreover, Fig.…”

Section: Modeling Resultsmentioning

confidence: 99%

“…Then, a de-normalizing procedure is proposed to return data to its normal scales. Then, a de-normalization procedure is employed to restore the data to its original scales 92 . Lastly, (GUI) has been developed, specifically tailored to the best prediction model, to aid engineers and researchers in effectively utilizing the best predictive model of this study.…”

Section: Methodsmentioning

confidence: 99%

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

Hameed,

Masood,

Srivastava

et al. 2024

Sci Rep

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Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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

Alam,

Shubham,

Kumar

et al. 2024

J Build Rehabil

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Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Cited by 9 publications

References 97 publications

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

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

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

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

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