1- Natural disaster
It is a natural event such as flood, earthquake or hurricane
that cause damage or loss of life. It effects both living and non-living.
2- Natural Disaster prediction
Natural disasters are inevitable in our world. Natural
disasters are of different types so it is difficult to predict each and every
one. Meteorologists can track a hurricane with precision, but
seismologists cannot predict
exactly when and where an earthquake will occur.
Prediction of disasters require extensive research and
funding. To predict a natural disaster we have to collect extensive past data,
record live data and generate patterns on previous data. By comparing past and
live data scientist predicts the future events to some extent. Trends are
calculated and used to predict earthquakes, tsunamis and volcanic eruptions.
We can also predict natural disasters by constant surveillance.
Using offshore cameras in hurricane prone areas ensures that strong winds and
waves can be recognized, that will help in tsunami predictions. Monitoring
ocean currents, weather predictions can be predicted in advance, warning nearby
areas in advance under the risk of hurricanes and tornados. But these short
term warnings are only effective when relief programs are planned and
effectively carried out. But this method is very costly and inefficient.
For cost effectiveness and timely information of natural disaster,
predicting it in advance is the only solution. However it is not always reliable,
because disasters unexpected and do not always follows trends. But it will save
much time and resource than constant surveillance.
3- AI techniques for earthquake predictions
Natural disasters like earthquakes are caused due to the propagating
seismic waves underneath the surface of earth. Seismometers are installed on different
geographical positions to record vertical motion of surface waves. Ground motion
types are divergence, convergence which results in transforming plate boundaries.
Major earthquakes are caused by divergence, convergence and transformation of
plate boundaries commonly known as faults. The origin where earthquake takes place is
origin point. Total sum of waves are calculated and time series data is
collected for further processing.
Four different aspects of this time series data with respect
to geophysical analysis can be considered for experimentation.
Analyze the earthquake data recording in
different time points independent of common source gather or common receiver
Analyze the earthquake data set in fixed or
variable length time intervals to predict different hidden patterns
Gathering layers data, like layer between Euro-Asian
and Indian plate etc, in time points to better analyze and study the seismic
patterns of layer with respect to time
Gather and analyze the earth lithosphere layer
data with respect to time intervals
Such identified characteristics of earthquake can be easily
scaled down using some activation function.
Figure 1: Illustration of criteria for fitness function
3.1 Feed Forward Neural Network
It is used with sigmoid function. Used on Seismic Electric signals, predicted magnitude and
pre-determined future seismic events. 80.55% accuracy Prediction of structural
responses for a structure. Prediction efficiency is 71%.
It is able
to predict both long and short term shocks. Outputs of different layers are not
3.2 Particle Swarm Optimization
PSO is used for building prior knowledge system. It is used for selection of input values
for the BPN (Back Propagation Neural
Network) based network. It can determine earthquake local earthquake
Works on the principles of Swarms of particles
searching for optimal solution in the defined search space. Converge to the
solution more efficiently then general BPN.
3.3 Genetic Algorithm
Rock mass stability is estimated for planning
purpose. Structural formation has been studied using GA.
Lower the data uncertainty. Used for building
settlement forecast after main shocks. Used in combination with support vector
machines for earthquake data set.
GA can work with improper or incomplete seismic
data. It is found highly efficient in prediction for future earthquakes. Commonly
used in research with different alterations.
Spatial clustering is used versus temporal
clustering for earthquake data sets. Spatial clustering has been identified in
data set while building earthquake forecast model using differential
Set of clusters is developed from huge set of
unsupervised data. This makes the overall scenario to be divided into many
sub-scenarios. Used in MSc algorithm with different aspect.
1.1 (Not complete summary update on other techniques)
Artificial Intelligence based techniques were widely used for earthquake time
series prediction. , the results of traditional approaches of probability
estimation should be enhanced by using the particle swarm optimization and
genetic algorithms based approaches. PSO and GA are capable to find actual
fault intensity in any particular region. This work is an attempt to cover
different strategies related to AI for earthquake prediction and crosscheck