Thermal Anomaly Detection prior to Earthquakes Combining Artificial Neural Networks Algorithms and Ant Colony Optimization

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Choubsaz Sepehr; AKHOONDZADEH MEHDI; Serajian Mohammad Reza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ENVIRONMENTAL HAZARDS MANAGEMENT Year:2015 | Volume:2 | Issue:2 Page(s): 207-224

Download Full-Text

Persian Verion

View:

850

Download:

Cites:

Information Journal Paper

Title

Thermal Anomaly Detection prior to Earthquakes Combining Artificial Neural Networks Algorithms and Ant Colony Optimization

Author(s)

Choubsaz Sepehr | AKHOONDZADEH MEHDI | Serajian Mohammad Reza | Issue Writer Certificate

Keywords

anomalyQ2

ant colony optimizationQ2

artificial neural networkQ1

earthquakeQ2

thermal pre-cursorQ2

Abstract

Remote sensing techniques made it possible to study thermal anomalies prior to major earthquakes regardless of complications in comprehending earthquake mechanisms. thermal pre-cursors are one of the main resources for earthquake prediction. In this paper, land surface temperature, atmospheric temperature, surface latent heat flux and outgoing long-wave radiation have been studied to detect anomalies prior to Varzaghan (August 11, 2012), Boushehr (April 9, 2013) and Saravan (April 16, 2013) earthquakes. To detect earthquake related anomalies, time series of each pre-cursor has been produced within the period of earthquake, land surface temperature and atmospheric temperature were acquired from MODIS products, surface latent heat flux from GLDAS library and outgoing long-wave radiation from AIRS products. These time series were predicted by an artificial neural network with ant colony optimization training method. The results of this study were compared with artificial neural network with Levenberg-Marquardt training algorithm. It has been shown that 10 to 13 days before Varzaghan earthquake, anomalies has appeared in all of the mentioned precursors, in case of Boushehr earthquake 6 to 9 days before the event, anomalies appeared in atmospheric temperature and outgoing long-wave radiation and also a strong anomaly appeared in surface latent heat flux 2 days prior to earthquake and in Saravan earthquakes anomalies have been detected 5 to 8 days before the earthquake in all of the studied thermal pre-cursors.

Cites

No record.

References

No record.

Cite

APA: Copy

Choubsaz, Sepehr, AKHOONDZADEH, MEHDI, & Serajian, Mohammad Reza. (2015). Thermal Anomaly Detection prior to Earthquakes Combining Artificial Neural Networks Algorithms and Ant Colony Optimization. ENVIRONMENTAL HAZARDS MANAGEMENT, 2(2 ), 207-224. SID. https://sid.ir/paper/265009/en

Vancouver: Copy

Choubsaz Sepehr, AKHOONDZADEH MEHDI, Serajian Mohammad Reza. Thermal Anomaly Detection prior to Earthquakes Combining Artificial Neural Networks Algorithms and Ant Colony Optimization. ENVIRONMENTAL HAZARDS MANAGEMENT[Internet]. 2015;2(2 ):207-224. Available from: https://sid.ir/paper/265009/en

IEEE: Copy

Sepehr Choubsaz, MEHDI AKHOONDZADEH, and Mohammad Reza Serajian, “Thermal Anomaly Detection prior to Earthquakes Combining Artificial Neural Networks Algorithms and Ant Colony Optimization,” ENVIRONMENTAL HAZARDS MANAGEMENT, vol. 2, no. 2 , pp. 207–224, 2015, [Online]. Available: https://sid.ir/paper/265009/en

Related Journal Papers