Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM

Applied Soft Computing, Journal Year: 2021, Volume and Issue: 109, P. 107541 - 107541

Published: May 31, 2021

Language: Английский

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Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Landslides, Journal Year: 2022, Volume and Issue: 19(10), P. 2489 - 2511

Published: June 30, 2022

Abstract Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) particle swarm (PSO) and water cycle (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), support vector regression (SVR), nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, convergence. results obtained Shuping Baishuihe landslides demonstrate that can be utilized determine optimum hyperparameters present statistical significance, thus enhancing accuracy reliability ML-based Significant differences observed among metaheuristics. Based on test, which was performed root mean square error (RMSE), Kling-Gupta efficiency (KGE), PSO recommended hyperparameter tuning SVR-based prediction due its ability maintain balance between precision, robustness. promising presenting

Language: Английский

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Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 615, P. 128618 - 128618

Published: Nov. 8, 2022

Language: Английский

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Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129480 - 129480

Published: April 12, 2023

Language: Английский

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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

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Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Hydrology, Journal Year: 2023, Volume and Issue: 10(3), P. 58 - 58

Published: Feb. 27, 2023

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, standardized precipitation index (SPI) was monitored predicted Peru between 1990 2015. The study proposed a hybrid model, called ANN-FA, for SPI time scales (SPI3, SPI6, SPI18, SPI24). A state-of-the-art firefly algorithm (FA) has been documented as powerful tool to support modeling issues. ANN-FA uses an artificial neural network (ANN) which is coupled with FA Lima via other stations. Through intelligent utilization series from neighbors’ stations model inputs, suggested approach might be used forecast at meteorological station insufficient data. To conduct this, SPI3, SPI24 were modeled using stations’ datasets Peru. Various error criteria employed investigate performance model. Results showed that effective promising drought also multi-station strategy lack results can help predict mean absolute = 0.22, root square 0.29, Pearson correlation coefficient 0.94, agreement 0.97 testing phase best estimation (SPI3).

Language: Английский

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Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(9), P. 12346 - 12407

Published: Feb. 12, 2024

Language: Английский

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Advances in Spotted Hyena Optimizer: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2021, Volume and Issue: 29(3), P. 1569 - 1590

Published: July 5, 2021

Language: Английский

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Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

Sustainability, Journal Year: 2020, Volume and Issue: 12(21), P. 8932 - 8932

Published: Oct. 27, 2020

Accurate information about groundwater level prediction is crucial for effective planning and management of resources. In the present study, Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed seasonal table depth (GWTD) in area between Ganga Hindon rivers located Uttar Pradesh State, India. A total 18 models both seasons (nine pre-monsoon nine post-monsoon) have been formulated by using recharge (GWR), discharge (GWD), previous data from 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability evaluated against traditional GA based on statistical indicators visual inspection. results appraisal indicates that outperformed predicting GWTD study region. Overall, GA-ANN-8 model an 8-9-1 structure (i.e., 8: inputs, 9: neurons hidden layer, 1: output) nominated optimal during pre- post-monsoon seasons. Additionally, it noted maximum number input variables approach improved accuracy. conclusion, proposed model’s findings could be readily transferable or implemented other parts world, specifically those similar geology hydrogeology conditions sustainable resources management.

Language: Английский

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Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms

Applied Energy, Journal Year: 2022, Volume and Issue: 316, P. 119063 - 119063

Published: April 19, 2022

Language: Английский

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