Dynamic Levy Flight Chimp Optimization

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 235, P. 107625 - 107625

Published: Oct. 22, 2021

Language: Английский

---

MEALPY: An open-source library for latest meta-heuristic algorithms in Python

Journal of Systems Architecture, Journal Year: 2023, Volume and Issue: 139, P. 102871 - 102871

Published: April 6, 2023

Language: Английский

---

A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 114, P. 105150 - 105150

Published: July 7, 2022

Machine learning (ML) has been extensively applied to model geohazards, yielding tremendous success. However, researchers and practitioners still face challenges in enhancing the reliability of ML models. In present study, a systematic framework combining k-fold cross-validation (CV), metaheuristics (MHs), support vector regression (SVR), Friedman Nemenyi tests was proposed improve performance geohazard modeling. The average normalized mean square error (NMSE) from CV sets adopted as fitness metric. Twenty most well-established MHs recent were tune hyperparameters SVR evaluated through nonparametric post hoc identify significant differences. Observations typical reservoir landslide selected benchmark dataset, accuracy, robustness, computational time, convergence speed compared. Significant differences among twenty identified by absolute (MAE), root squared (RMSE), Kling–Gupta efficiency (KGE), with p values lower than 0.05. comparison results demonstrated that multiverse optimizer (MVO) is highest-performing, stable, computationally efficient algorithms, providing superior other methods, nearly optimum correlation coefficient (R), low MAE (23.5086 versus 23.9360), RMSE (48.6946 50.1882), high KGE (0.9803 0.9893) predicting displacement Shuping landslide. This paper considerably enriches literature regarding hyperparameter optimization algorithms enhancement their reliability. addition, have potential for evaluating comparing various ML-based

Language: Английский

---

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

---

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

---

Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 82, P. 16 - 25

Published: Sept. 29, 2023

Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn improve based on data. Deep uses neural networks to complex data patterns relationships. A combination satellite imagery, radar data, ground-based observations are used using aircraft or satellites, remote sensing (RS) collects distant objects locations. Satellites gather regional precipitation for hybrid models. An algorithm trained historical rainfall measurements would then process monitoring instrument input features, machine-learning can predict precipitation. Evaluation regression methods is degree agreement between predicted observed values. The RMSE, R2, MAE statistical measures check precision prediction forecasting model. Machine excels at regardless climate timescale. As one more popular predicting rainfall, LSTM demonstrate their superiority. Remote should be investigated further due scarcity.

Language: Английский

---

Medium-long-term prediction of water level based on an improved spatio-temporal attention mechanism for long short-term memory networks

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 618, P. 129163 - 129163

Published: Jan. 24, 2023

Language: Английский

---

Investigating photovoltaic solar power output forecasting using machine learning algorithms

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2022, Volume and Issue: 16(1), P. 2002 - 2034

Published: Oct. 3, 2022

Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development required determine the best machine learning (ML) algorithm for PV solar output forecasting. Existing studies have established superiority of artificial neural network (ANN) random forest (RF) algorithms field. However, more recent demonstrated promising forecasting performances by decision tree (DT), extreme gradient boosting (XGB), long short-term memory (LSTM) algorithms. Therefore, present study aims a gap field determining performer among these 5 A data set from United States' National Renewable Energy Laboratory (NREL) consisting parameters monocrystalline silicon module Cocoa, Florida was utilized. Comparisons scores show that ANN superior as ANN16 model produces mean absolute error (MAE), root squared (RMSE) coefficient determination (R2) with values 0.4693, 0.8816 W, 0.9988, respectively. It concluded most reliable applicable

Language: Английский

---

Optimization of constraint engineering problems using robust universal learning chimp optimization

Advanced Engineering Informatics, Journal Year: 2022, Volume and Issue: 53, P. 101636 - 101636

Published: May 30, 2022

Language: Английский

---

Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: March 10, 2022

Abstract Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due this, the study SF prediction is highly significant for purpose municipal damage mitigation. In present study, machine learning (ML) models based on support vector (SVM), artificial neural network (ANN), long short-term memory (LSTM), tested developed predict 11 different rivers throughout Malaysia. data sets were collected from Malaysian Department Irrigation Drainage. The main objective propose a universal model most capable predicting SFs within Based findings, ANN3 which was using ANN algorithm input scenario 3 (inputs consisting previous days SF) deduced as best overall ML it outperformed all other 4 out sets; obtained among highest average RMs with score 3.27, hence indicating very adaptable reliable accurately river case studies. Therefore, proposed

Language: Английский

---