Assignment Prediction By: Your Name Table of Contents

 

 

 

Assignment No 2

 

AI techniques for
Natural Disasters Prediction

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By: Your Name

Table of Contents

1-    Natural disaster                                                                                    
3

2-    Natural disaster prediction                                                                  3

3-    AI techniques for Earthquake
predictions                                        3

3.1- Feed forward neural network                                                    
4

3.2- Particle swarm optimization                                                      
4

3.3- Genetic Algorithm                                                                        
4

3.4- Clustering                                                                                
       5

4-    AI techniques for Water storm
predictions                                     
5

4.1- Nonlinear ensemble prediction                                                 
5

4.2- Back propagation neural network                                              6

5-    Degree of success in natural disaster
prediction by AI                   7

6-    Summary                                                                                                
8

7-    References                                                            
                                 9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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.

There are four different aspects of this time series data
with respect to geophysical analysis can be considered for experimentation.

1.      
Analyze the earthquake data recording in
different time points independent of common source gather or common receiver
gather recordings.

2.      
Analyze the earthquake data set in fixed or
variable length time intervals to predict different hidden patterns

3.      
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

4.      
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. FFN is used on Seismic Electric signals, predicted magnitude and
pre-determined future seismic events. Prediction of structural responses for a
structure has 80.55% accuracy. Prediction has 71%
efficiency.

It is able
to predict both long and short term shocks. Outputs of different layers are not
feedback.

 

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
location.

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.

 

3.4                       
– Clustering

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
probability.

 

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.

 

Until recently,
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
their reliability.

.

 

4-  AI
techniques for Water Storms Prediction

Water storms occur due to intense
unsustainable winds in oceans. Hurricanes, cyclones and typhoons are all water storms
but their name is different due to the geographical location of storm.

There are different artificial
intelligence techniques to predict storms. Some are given below.

4.1                       
– Nonlinear AI ensemble
prediction

A new nonlinear artificial
intelligence ensemble prediction (NAIEP) model has been developed for
predicting typhoon intensity based on multiple neural networks with the same
expected output and using an evolutionary genetic algorithm (GA).

Ensemble numerical prediction
(ENP) model, whether created with different physical process parameterization
schemes or with different initial conditions from a Monte Carlo approach,
formally consists of many different ensemble members. By optimizing the network
structure and the connection weight of ANNs, genetic evolution is able to
create a number of different neural network individuals.

Ensemble prediction of NWP is
motivated by the fact that NWP forecasts are sensitive both to small
uncertainties in the initial conditions and model errors, so it is hard to
further improve the accuracy of single model deterministic predictions.

To construct an NAIEP model, a
number of individual neural networks are first created and then integrated to
build an ensemble prediction model.

A GA is used to construct the
members of the ensemble, and a three-layer back-propagation (BP) network is
used as the basic model for the neural networks, the major computational steps
are summarized below:

1.      
Randomly
generate the connection weights and thresholds from input layer to hidden layer
and from hidden layer to output layer, and set the global convergence error, ?,
of the model.

2.      
Perform
supervised learning training of the network with learning matrix samples,
calculate the error between the real input and expected output of the network,
and tune the connection weight coefficients from input layer to hidden layer
and from hidden layer to output layer using the learning algorithm of the
error-inverse propagation of the BP network.

3.      
If the
calculated output error of the model is greater than ?, return to step 2;
otherwise, end the training and compute the prediction value using the
connection weights, thresholds of the network, and predictors of the prediction
samples.

The meteorological ensemble
modeling approach of GNN opens up a vast range of possibilities for operational
weather prediction.

 

4.2                       
– Back propagation Neural Network

Like human neural network in
artificial neural network has 3 layers; perceptron, dendrites and axon. In NN,
each input is multiplied by its weight of its connection of neuron. Connection
determine which input has to be forward and then it sums up all the inputs.
Then it is passed through the hidden layer to calculate its results. After it
passes the result to output layer. In back propagation NN, there is only one
input layer, one output layer and a hidden layer. It is easy to calcite the
results.

To compute the prediction of
storm or any other disaster information is collected and then it is feed to the
neural network. First of all data is normalized then it is feed to input layer.
From input layer the data is transferred to hidden layer. There we do our
calculations by applying sigmoid function. From hidden layer calculations are
collected and summed up, this sum is input to output layer.

NN with back propagation is a
self-driving system which collects data then train itself for different
conditions and scenarios and produce results.

NN with back propagation and
other NN’s are not more than pattern recognition techniques. They are just some
short term predictive skills not to replace metrologies. But it can help in
understanding metrological problems and can solve many complicated patterns
that are difficult to solve by humans and simple programs.

 

5-  Degree
of success in natural disaster prediction by AI

For some people weather forecasts
are just for surety of good day ahead. But for some people it is everything.
Their bread and butter depends upon it. By applying artificial intelligence
knowledge we have been able to transforms life of many people and giving them a
new chance.

 Companies and governments are collecting data
of winds, water and soil from satellites and different devices installed on different
portions on earth. Physical understanding of environment with the combination
of artificial intelligence improves prediction skills for multiple types of
natural disasters like thunderstorms, tornadoes, hurricanes, volcanic eruptions,
earthquakes and many more.

Hail forecasting can be
forecasted accurately by using machine learning according to research paper. Hail
cause billions of dollars damages every year. Many cars, airplanes and
buildings are effected in hail storms. By forecasting hail storms cars can be
moved to safe places, flights can be delayed and prevention measures can be
taken for buildings to protect them from heavy damages. IBM is commercializing
its weather forecasting for that purpose so warning can be issued to people from
heavy damage.

Big companies like IBM, Panasonic
are working on their weather forecasting systems and increasing its accuracy day
by day by applying new developed and refined artificial intelligence and computational
intelligence techniques. Better weather forecasting allows airlines to change
their routes and save money and time, improve safety and increase on time
arrivals.

Better weather predictions has
direct effect on different fields of life. It directly effects the agriculture,
90% of crops are destroyed by weather conditions. It can be controlled by
proper weather conditions. If damage is inevitable then we can save our money
and time by not planting that kind of crops that are not suitable for that kind
of weather condition.

Transportation is improved by
weather forecasting a lot. Directing routes of flights in near storm save many
live and money than ever. Road transport is improved by making optimized routes
for hilly and severe condition areas. The company using Panasonic’s weather
forecasting shows significant rise in commercial aircrafts climb profile
optimization. . They claim their system can reduce fuel consumption by up to 10
percent during ascent. By putting in that perspective, US airline carriers
spent $24.6 billion on fuel last year.

Better weather forecasting saves many
lives and helps to speed up the rebuilding efforts. Companies like IBM and
Panasonic has started to combine their weather forecasting tools with information
about utilities’ distribution networks and data about local ground cover for
severe storms. Machine learning helps them to predict many sever conditions and
outages. IBM claims that their prediction is about 70- 80 % accurate and is 72
hours before the disaster.

Weather related data source will
continue to grow dramatically and the new advances in machine learning are
making it possible for government agencies and companies to make better use of
all this data. Weather is always unpredicted and it can never be truly
perfected, but AI will allow to make consistent improvement in its accuracy and
in its resolution.

 

More refined and localized
weather information will help to make it easier to find distinct patterns and
connections in them. Small improvements in weather forecasting has huge impact
on the modern day world. These improvements will give many companies and
government agencies useful pieces of data by finding new correlations and
giving companies and government agencies more chances to take full advantage of
them.  

6-  Summary

Natural disasters are inevitable
and unpredicted in nature. Nobody can exactly predict that what will happen
next. But by the passage of time human being is able to extract information
from past events and made patterns from that information. In past those patterns
were not so clear and difficult to compute. Modern day technology helps to
collect data and draws results from that data effectively.

Artificial intelligence plays a
key role in pattern recognition and analysis of past events to predict future
events. Different techniques like Neural network (NN), genetic algorithm (GN),
particle swarm optimization (PSO), clustering and many more helps us to find patterns
and prediction. These algorithms alone do not generate good results. But by
merging two algorithms gives us better results that give us better
understanding of occurring and future events.

By using artificial intelligence
techniques success rate of prediction is about 60-70%. Although it is not so
accurate one but it helps to save resources and lives. By using weather
prediction crops are not destroying any more than before. Air traffic is
controlled in a good manner and they are informed before any bad can happen to
them. In short, artificial intelligence made a good impact on the life of
people by giving them useful piece of information in advance. 

 

 

 

 

 

 

 

 

 

 

7-  References

Aberson, S. D., and C. R.
Sampson, 2003: On the
predictability of tropical cyclone tracks in the northwest Pacific basin.
Mon. Wea. Rev., 131, 1491–1497.

Chen, G-L., X-F. Wang, and Z-Q. Zhuang, 1996: Genetic
Algorithm and Application. (in Chinese). Beijing Communication Press, 178
pp.

Allen J. F.
Time and time again: the many ways to represent time. International
Journal of Intelligent Systems. 1991;6(4):341–355.
doi: 10.1002/int.4550060403

Breiman L.
Random forests. Machine Learning. 2001;45(1):5–32.
doi: 10.1023/A:1010933404324.

Davies-Jones
R. P. Tornado dynamics. In: Kessler E., editor. Thunderstorm
morphology and dynamics. Norman: University of Oklahoma
Press; 1986. pp. 197–236.

U.S. Dept. of Commerce/NO A A: National Hurricane Operation
Plan: Federal Coordination For Metrology Services and Supporting Research,
Washington D.C. 1992.

 

D. E. Rumelhart, J. L. McClelland, “Exploration in the
Microstructure of Cognition” in Parallel Distributed Processing, The MIT
Press, vol. 1, 1986

S. Y. Pakkala, F. C. Lin, Proceedings of the SPIE Conference,
1992-April-2124.

Douilly
R., Haase J. S., Ellsworth W. L., Bouin M. P., Calais E., Symithe S. J.,
Armbruster J. G., De Lépinay B. M., Deschamps A. and Mildor S. L., Crustal
Structure and Fault Geometry of the 2010 Haiti Earthquake from

Temporary Seismometer
Deployments, Bulletin of the Seismological Society of America,
103(4),2305-2325 (2013)

Klein E. M., Geochemistry of the
Igneous Oceanic Crust, Treatise on Geochemistry, 3, 433-463 (2003)

Holtzman B. K. and Kendall J.,
Organized melt, seismic anisotropy, and plate boundary lubrication, Geochemistry,
Geophysics, Geosystems, 11(12), 1-29 (2010)