ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 3-11, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-8/3/2016/
doi:10.5194/isprs-annals-III-8-3-2016
 
07 Jun 2016
LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (Mw = 7.5) OF 25 APRIL 2015
M. Akhoondzadeh Remote Sensing Department, School of Surveying and Geospatial Engineeringl, University college of Engineering, University of Tehran, North Amirabad Ave., Tehran, Iran
Keywords: Ionosphere, TEC, earthquake, anomaly, LSSVM Abstract. Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth = 15.0 km) a powerful earthquake of Mw = 7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.
Conference paper (PDF, 762 KB)


Citation: Akhoondzadeh, M.: LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (Mw = 7.5) OF 25 APRIL 2015, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 3-11, doi:10.5194/isprs-annals-III-8-3-2016, 2016.

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