The aim
of this study is to evaluate the performance of BP neural network techniques in
predicting earthquakes occurring in the region of Himalayan belt (with the use
of different types of input data). These
parameters are extracted from Himalayan Earthquake catalogue comprised of all
minor, major events and their aftershock sequences in the Himalayan basin for
the past 128 years from 1887 to 2015. This data warehouse contains event data,
event time with seconds, latitude, longitude, depth, standard deviation and magnitude.
These field data are converted into eight mathematically computed parameters known as
seismicity indicators. These seismicity indicators have been used to train the
BP Neural Network for better decision making and predicting the magnitude of
the pre-defined future time period. These mathematically computed indicators
considered are the clustered based on every events above 2.5 magnitude, total
number of events from past years to 2014, frequency-magnitude
distribution b-values, Gutenberg-Richter inverse power law curve for the n
events, the rate of square root of seismic energy released during the n events,
energy released from the event, the mean square deviation about the regression
line based on the Gutenberg-Richer inverse power law for the n events,
coefficient of variation of mean time and average value of the magnitude for
last n events. We propose a three-layer feed forward BP neural network model to
identify factors, with the actual occurrence of the earthquake magnitude M and
other seven mathematically computed parameters seismicity indicators as input
and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue
comprised of all events above magnitude 2.5 mg, their aftershock sequences in
the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the
earthquakes of magnitude between 4.0 and 6.0.
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