Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Shi, Lamei; Zhang, Jiahua; Zhang, Da; Igbawua, Tertsea; Liu, Yuqin

doi:10.1016/j.asr.2019.11.027

Cited by 26 publications

(17 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 5: For any glowworm i , the probability ) (t P ij of its moving to each neighbor is calculated according to equation (27). A neighbor is selected according to the probability to move, and then location of it is updated based on equation (26).…”

Section: ) Training Algorithm Of Twin Curvelet Support Vector Machinementioning

confidence: 99%

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

et al. 2021

View full text Add to dashboard Cite

The energy and resources saving has become a major task of petrochemical enterprises, it is necessary to construct the energy saving diagnostic system for understand the real time operation information of petrochemical plant and provide theoretical basis for taking energy saving measures. The energy saving diagnosis process of petrochemical plant based on twin Curvelet support vector machine optimized by hybrid glowworm swarm algorithm is designed. The Curvelet kernel function is constructed based on curvelet transform to establish theory model of twin curvelet support vector machine. In order to improve the prediction precision of the twin curvelet support vector machine, the hybrid glowworm swarm optimization algorithm is constructed based on simulated annealing simulation to optimize the parameters of the twin Curvelet support vector machine. Finally, a petrochemical plant is used as research object to carry out diagnosis simulation analysis, and results showed that the proposed prediction model can effectively improve diagnostic effectiveness of the energy saving effect of petrochemical plant.

show abstract

Section: ) Training Algorithm Of Twin Curvelet Support Vector Machinementioning

confidence: 99%

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The recent advances of artificial intelligence have inspired several attempts to develop dust detection algorithm for passive sensors, in particular MODIS, using machine-learning (ML) or Deep-Learning (DL) methods [26][27][28][29][30]. These ML algorithms have demonstrated excellent skills.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, their training dataset is often based on "physically-based" methods from passive sensors. For example, Shi et al (2020) developed a SVM-based algorithm to detect dust storms from MODIS satellite image and they used the UV aerosolindex from the OMI (Ozone Monitoring Instrument) on board the Aura satellite, also part of the A-Train, to assess the detection results [29]. As mentioned above, the physically-based methods for passive sensors often suffer from a variety of problems, which could in turn influence the ML-based methods if they are used as the training and/or testing dataset.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

et al. 2021

View full text Add to dashboard Cite

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

show abstract

“…The recent advances of artificial intelligence have inspired several attempts to develop dust detection algorithm for passive sensors, in particular MODIS, using machine-learning (ML) or Deep-Learning (DL) methods [20][21][22][23]. These ML and DL based algorithms have demonstrated excellent skills.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, their training dataset is often based on "physically-based methods from passive sensors. For example, Shi et al (2020) developed a SVM-based algorithm to detect dust storm from MODIS satellite image and they used the UV aerosol-index from the OMI (Ozone Monitoring Instrument) on board of Aura satellite, also part of the A-Train, to assess the detection results [22]. As mentioned above, the physically-based methods for passive senors often suffer from a variety of problems, which could in turn influence the ML-based methods if they are used as the training and/or testing dataset.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Algorithms for Global Dust Aerosol Detection From Satellite Images: Inter-Comparisons and Evaluation

Lee¹,

Shi²,

Cai³

et al. 2020

Preprint

View full text Add to dashboard Cite

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed 5 different machine-learning (ML) and deep-learning (DL) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML and DL algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicates that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81 %, 89 % and 85 % over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML and DL algorithms to NOAA’s Aerosol Detection Product (ADP) , which is a product that classifies dust, smoke and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML and DL methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

show abstract

Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Cited by 26 publications

References 64 publications

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

Energy saving diagnosis model of petrochemical plant based on intelligent curvelet support vector machine

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Machine Learning Based Algorithms for Global Dust Aerosol Detection From Satellite Images: Inter-Comparisons and Evaluation

Contact Info

Product

Resources

About