IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5333-d1099971.html
   My bibliography  Save this article

Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels

Author

Listed:
  • Elmira Asadi-Fard

    (Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor 46417-76489, Iran)

  • Samereh Falahatkar

    (Department of Environmental Sciences, Faculty of Natural Resources, Tarbiat Modares University, Noor 46417-76489, Iran)

  • Mahdi Tanha Ziyarati

    (Department of Health, Safety and Environment Engineering, Ferdous Rahjoyan Danesh Higher Education Institute, Borazjan 75617-86118, Iran
    Department of Health, Safety and Environment, Pars Special Economic Energy Zone, Asalouyeh 75119-46484, Iran)

  • Xiaodong Zhang

    (Division of Marine Science, School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center, MS 39529, USA)

  • Mariapia Faruolo

    (Institute of Methodologies for Environmental Analysis, National Research Council, 85050 Tito Scalo, Italy)

Abstract

The environment, the climate and human health are largely exposed to gas flaring (GF) effects, releasing significant dangerous gases into the atmosphere. In the last few decades, remote sensing technology has received great attention in gas flaring investigation. The Pars Special Economic Energy Zone (PSEEZ), located in the south of Iran, hosts many natural oil/gas processing plants and petrochemical industries, making this area one of the most air-polluted zones of Iran. The object of this research is to detect GF-related thermal anomalies in the PSEEZ by applying, for the first time, the Reed-Xiaoli Detector (RXD), distinguished as the benchmark algorithm for spectral anomaly detection. The RXD performances in this research field have been tested and verified using the shortwave infrared (SWIR) bands of OLI-Landsat 8 (L8), acquired in 2018 and 2019 on the study area. Preliminary results of this automatic unsupervised learning algorithm demonstrated an exciting potential of RXD for GF anomaly detection on a monthly scale (75% success rate), with peaks in the months of January and February 2018 (86%) and December 2019 (84%). The lowest detection was recorded in October 2019 (48%). Regarding the spatial distribution of GF anomalies, a qualitatively analysis demonstrated the RXD capability in mapping the areas affected by gas flaring, with some limitations (i.e., false positives) due to possible solar radiation contribution. Further analyses will be dedicated to recalibrate the algorithm to increase its reliability, also coupling L8 and Landsat 9, as well as exploring Sentinel 2 SWIR imagery, to overcome some of the observed RXD drawbacks.

Suggested Citation

  • Elmira Asadi-Fard & Samereh Falahatkar & Mahdi Tanha Ziyarati & Xiaodong Zhang & Mariapia Faruolo, 2023. "Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5333-:d:1099971
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5333/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5333/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    2. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    3. Davide Nicola Continanza & Andrea del Monaco & Marco di Lucido & Daniele Figoli & Pasquale Maddaloni & Filippo Quarta & Giuseppe Turturiello, 2023. "Stacking machine learning models for anomaly detection: comparing AnaCredit to other banking data sets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.
    4. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
    5. Priyanga Dilini Talagala & Rob J Hyndman & Catherine Leigh & Kerrie Mengersen & Kate Smith-Miles, 2019. "A Feature-Based Framework for Detecting Technical Outliers in Water-Quality Data from In Situ Sensors," Monash Econometrics and Business Statistics Working Papers 1/19, Monash University, Department of Econometrics and Business Statistics.
    6. Cian Ryan & Finbarr Murphy & Martin Mullins, 2019. "Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1125-1140, May.
    7. Kenichiro Nagata & Toshikazu Tsuji & Kimitaka Suetsugu & Kayoko Muraoka & Hiroyuki Watanabe & Akiko Kanaya & Nobuaki Egashira & Ichiro Ieiri, 2021. "Detection of overdose and underdose prescriptions—An unsupervised machine learning approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    8. Shuo Xu & Liyuan Hao & Xin An & Dongsheng Zhai & Hongshen Pang, 2019. "Types of DOI errors of cited references in Web of Science with a cleaning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1427-1437, September.
    9. Milan Miric & Hakan Ozalp & Erdem Dogukan Yilmaz, 2023. "Trade‐offs to using standardized tools: Innovation enablers or creativity constraints?," Strategic Management Journal, Wiley Blackwell, vol. 44(4), pages 909-942, April.
    10. Parminder Singh & Sujatha Krishnamoorthy & Anand Nayyar & Ashish Kr Luhach & Avinash Kaur, 2019. "Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    11. Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
    12. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    13. Taha Yehia & Ali Wahba & Sondos Mostafa & Omar Mahmoud, 2022. "Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis," Energies, MDPI, vol. 15(23), pages 1-25, November.
    14. Timothy DeLise, 2023. "Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market," Papers 2309.00088, arXiv.org.
    15. VANHOEYVELD, Jellis & MARTENS, David, 2018. "Towards a scalable anomaly detection with pseudo-optimal hyperparameters," Working Papers 2018012, University of Antwerp, Faculty of Business and Economics.
    16. Wordliczek Lukasz, 2021. "Between incrementalism and punctuated equilibrium: the case of budget in Poland, 1995–2018," Central European Journal of Public Policy, Sciendo, vol. 15(2), pages 14-30, December.
    17. Priyanga Dilini Talagala & Rob J Hyndman & Kate Smith-Miles, 2019. "Anomaly Detection in High Dimensional Data," Monash Econometrics and Business Statistics Working Papers 20/19, Monash University, Department of Econometrics and Business Statistics.
    18. Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
    19. Ruhi Kiran Bajaj & Rebecca Mary Meiring & Fernando Beltran, 2023. "Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study," Future Internet, MDPI, vol. 15(3), pages 1-15, March.
    20. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5333-:d:1099971. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.