Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

Boroughani, Mahdi; Pourhashemi, Sima; Hashemi, Hossein; Salehi, Mahdi; Amirahmadi, Abolghasem; Asadi, Mohammad Ali Zangane; Berndtsson, Ronny

doi:10.1016/j.ecoinf.2020.101059

Cited by 71 publications

(33 citation statements)

References 62 publications

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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%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.

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Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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“…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%

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

Moreno-Ruiz

García-Lázaro

Arbeló

et al. 2020

Sensors

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This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms.

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“…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%

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

Lee¹,

Shi²,

Cai³

et al. 2020

Preprint

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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.

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Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping

Cited by 71 publications

References 62 publications

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

MODIS Sensor Capability to Burned Area Mapping—Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions

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

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