Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

Liu, Chunhao; Pan, Guangyuan; Song, Dongming; Yang, Zhi

doi:10.1109/access.2023.3291146

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…After inputting the samples, the samples in the action of this mapping arrive at a linearly divisible space. KELM uses the kernel function to replace the mapping of the hidden node of ELM to map the input data into a high-dimensional space [53], which can better adapt to complex data distributions and deal with nonlinear problems. Compared with traditional neural network algorithms, KELM overcomes the problem of dimensionality catastrophe while avoiding the drawbacks of the instability of ELM, improving generalization performance and nonlinear approximation abilities of conventional models [54].…”

Section: Modeling and Verificationmentioning

confidence: 99%

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Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Miao,

Chen,

Guo

et al. 2024

Remote Sensing

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Anthocyanin can improve the stress tolerance and disease resistance of winter wheat to a certain extent, so timely and accurate monitoring of anthocyanin content is crucial for the growth and development of winter wheat. This study measured the ground-based hyperspectral reflectance and the corresponding anthocyanin concentration at four key growth stages—booting, heading, flowering, and filling—to explore the spectral detection of anthocyanin in winter wheat leaves. Firstly, the first-order differential spectra (FDS) are obtained by processing based on the original spectra (OS). Then, sensitive bands (SBS), the five vegetation indices for optimal two-band combinations (VIo2), and the five vegetation indices for optimal three-band combinations (VIo3) were selected from OS and FDS by band screening methods. Finally, modeling methods such as RF, BP, and KELM, as well as models optimized by genetic algorithm (GA), were used to estimate anthocyanin content at different growth stages. The results showed that (1) among all the models, the GA_RF had incredible performance, VIo3 was the superior parameter for estimating anthocyanin values, and the model GA_RF of FDS data based on VIo3 for the filling stage (Rv2 = 0.950, RMSEv = 0.005, RPDv = 4.575) provided the best estimation of anthocyanin. (2) the first-order differential processing could highlight the degree of response of SBS, VIo2, and VIo3 to the anthocyanin values. The model performances of the FDS were better than that of OS on the whole, and the Rv2 of the optimal models of FDS were all greater than 0.89. (3) GA had optimizing effects on the RF, BP, and KELM, and overall, the GA models improved the R2 by 0.00%-18.93% compared to the original models. These results will provide scientific support for the use of hyperspectral techniques to monitor anthocyanin in the future.

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Section: Modeling and Verificationmentioning

confidence: 99%

“…GA can optimize the parameters of RF, such as the number of decision trees and the tree depth [88], and others. GA can also optimize the parameters of BP and KELM, like searching for superior weights and bias value combinations [53,89].…”

Section: Optimizing Rf Bp and Kelm With Gamentioning

confidence: 99%

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Miao,

Chen,

Guo

et al. 2024

Remote Sensing

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“…Ma et al utilized the XGBoost method to forecast air quality in Shanghai [ 21 ]. Liu et al employed intelligent algorithms to seek optimal parameters, proposing a genetic algorithm-based extreme learning machine model [ 22 ]. Patel et al conducted sensitivity analysis to comprehend the individual factor impacts, subsequently employing a random forest model for predicting air quality in Delhi [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Xia,

Chen,

Wang

et al. 2024

Entropy

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The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction’s mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

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Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

Mohammed,

Karataev,

Oshiga

et al. 2024

Arch Computat Methods Eng

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Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

Cited by 13 publications

References 41 publications

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

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