2020
DOI: 10.1016/j.ecoinf.2020.101059
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Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

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Cited by 76 publications
(33 citation statements)
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“…RF was used to enhance the low cost sensors performance for the purposes of air quality monitoring by [ 56 ] as the model’s prediction results were satisfactory when compared to empirical models. Additionally, [ 57 ] used RF in conjunction with remote sensing techniques for the purpose of dust source detection and mapping. It outperformed other machine learning algorithms such as Weights of Evidence (WOE) and Frequency Ratio (FR).…”
Section: Materials and Methodsmentioning
confidence: 99%
“…RF was used to enhance the low cost sensors performance for the purposes of air quality monitoring by [ 56 ] as the model’s prediction results were satisfactory when compared to empirical models. Additionally, [ 57 ] used RF in conjunction with remote sensing techniques for the purpose of dust source detection and mapping. It outperformed other machine learning algorithms such as Weights of Evidence (WOE) and Frequency Ratio (FR).…”
Section: Materials and Methodsmentioning
confidence: 99%
“…To assess the impact of the PB on the accuracy of a BA classified map, the area enclosed by it (the Area Under the Pareto Boundary, AUPB) was calculated, by analogy with the area of the ROC (Receiver Operating Characteristic) curve used in other burned area studies using machine learning or data mining [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The ROC curve is a probability curve constructed from sensibility and 1-specificity pairs {(S i ,1-Sp i )}, obtained using a procedure similar to that of the PB, and the area enclosed by it, the Area Under the ROC Curve (AUC), is interpreted as a measure of the separability between the two classes considered from the selected p parameter.…”
Section: Methodsmentioning
confidence: 99%
“…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%
“…Although these emerging studies are very encouraging, they also have some limitations. These studies either focused only on certain geographical regions (e.g., only Iran and Asian regions in [20]) or investigated only a few number of cases (e.g., only 31 dust events are studied in [23]). In addition, their training dataset is often based on "physically-based methods from passive sensors.…”
Section: Introductionmentioning
confidence: 99%