Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction

Erden, Caner

doi:10.1007/s13762-023-04763-6

Cited by 31 publications

(12 citation statements)

References 99 publications

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“…Compared to standalone models, hybrid machine learning models have demonstrated high flexibility, increased interpretability, and improved performance in terms of forecasting PM 2.5 concentrations 24 , 25 . This is because they combine the strengths of multiple algorithms and can capture complex relationships between variables that a single algorithm may miss 26 . Since hybrid machine learning models can adapt to changes in the data by adjusting the weights of the algorithms, they are more robust to changes in input data than standalone models.…”

Section: Introductionmentioning

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: Introductionmentioning

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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show abstract

“…Convolutional Neural Networks (CNNs) have been employed for their ability to extract spatial patterns and correlations from complex air quality data sets, taking into account geographical features and meteorological variables 54 . Recurrent Neural Networks (RNNs), particularly Long Short‐Term Memory (LSTM) networks, have proven effective in capturing temporal dependencies and patterns in time series PM 2.5 data, making them valuable for accurate forecasting 55–57 . Hybrid techniques, such as CNN‐LSTM, combine the strengths of both CNNs and LSTMs, enabling simultaneous modeling of spatial and temporal relationships.…”

Section: Resultsmentioning

confidence: 99%

“…Three criteria, including multiple coefficients of determination (R 2 ), MAE, and RMSE, were used as error criteria. The data imputed by missForest revealed lower error metrics for [55][56][57] Hybrid techniques, such as CNN-LSTM, combine the strengths of both CNNs and LSTMs, enabling simultaneous modeling of spatial and temporal relationships. These hybrid models have demonstrated superior performance in predicting PM 2.5 levels by effectively leveraging spatial and temporal data analysis advantages.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Koçak

2024

Env Prog and Sustain Energy

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This study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back‐propagation (RPROP‐ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.

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“…This iterative process continues until a global optimum is reached, ensuring the optimized path's efficacy and adaptability [42,43]. The integration of DNNs and GAs not only realizes an effective fusion of data-driven and heuristic search methods but also significantly enhances the accuracy of the polishing trajectory optimization process [44][45][46]. This synergistic effect not only makes path optimization more precise but also dramatically speeds up the optimization process, achieving a speed increase of 300 to 2000 times compared to using a GA alone [47].…”

Section: Optimizing Grinding Trajectory Based On Deep Genetic Algorithmmentioning

confidence: 99%

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

Meng,

Zhou,

et al. 2023

Machines

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In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding.

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Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction

Cited by 31 publications

References 99 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

Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

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Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction

Cited by 31 publications

References 99 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

Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

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Prediction of daily fine particulate matter (PM_2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network