Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to deve...

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Autores principales:	Ítalo de Oliveira Matias, Patrícia Carneiro Genovez, Sarah Barrón Torres, Francisco Fábio de Araújo Ponte, Anderson José Silva de Oliveira, Fernando Pellon de Miranda, Gil Márcio Avellino
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	synthetic aperture radar (SAR) machine learning (ML) exploratory data analysis (EDA) classification model (CM) oil slicks source (OSS) oil seeps Science Q
Acceso en línea:	https://doaj.org/article/10e960cf8b374cba9e7883325556fc1d
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spelling	oai:doaj.org-article:10e960cf8b374cba9e7883325556fc1d2021-11-25T18:54:25ZImproved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach10.3390/rs132245682072-4292https://doaj.org/article/10e960cf8b374cba9e7883325556fc1d2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4568https://doaj.org/toc/2072-4292Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.Ítalo de Oliveira MatiasPatrícia Carneiro GenovezSarah Barrón TorresFrancisco Fábio de Araújo PonteAnderson José Silva de OliveiraFernando Pellon de MirandaGil Márcio AvellinoMDPI AGarticlesynthetic aperture radar (SAR)machine learning (ML)exploratory data analysis (EDA)classification model (CM)oil slicks source (OSS)oil seepsScienceQENRemote Sensing, Vol 13, Iss 4568, p 4568 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	synthetic aperture radar (SAR) machine learning (ML) exploratory data analysis (EDA) classification model (CM) oil slicks source (OSS) oil seeps Science Q
spellingShingle	synthetic aperture radar (SAR) machine learning (ML) exploratory data analysis (EDA) classification model (CM) oil slicks source (OSS) oil seeps Science Q Ítalo de Oliveira Matias Patrícia Carneiro Genovez Sarah Barrón Torres Francisco Fábio de Araújo Ponte Anderson José Silva de Oliveira Fernando Pellon de Miranda Gil Márcio Avellino Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
description	Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.
format	article
author	Ítalo de Oliveira Matias Patrícia Carneiro Genovez Sarah Barrón Torres Francisco Fábio de Araújo Ponte Anderson José Silva de Oliveira Fernando Pellon de Miranda Gil Márcio Avellino
author_facet	Ítalo de Oliveira Matias Patrícia Carneiro Genovez Sarah Barrón Torres Francisco Fábio de Araújo Ponte Anderson José Silva de Oliveira Fernando Pellon de Miranda Gil Márcio Avellino
author_sort	Ítalo de Oliveira Matias
title	Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
title_short	Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
title_full	Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
title_fullStr	Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
title_full_unstemmed	Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach
title_sort	improved classification models to distinguish natural from anthropic oil slicks in the gulf of mexico: seasonality and radarsat-2 beam mode effects under a machine learning approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/10e960cf8b374cba9e7883325556fc1d
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Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

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