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A Mobile Disaster Management System Using the Android Technology pdf
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A Mobile Disaster Management System Using the Android Technology

.pdf  A Mobile Disaster Management.pdf (Size: 1.36 MB / Downloads: 65)


The Philippines is one of the countries in the world vulnerable to natural hazards because of its geographic
location. It also lacks an efficient disaster management system that will help in times of need. One common scenario
during disasters is that the activity of rescue and relief is not well-coordinated. For this reason, there is a need for a
system that will help in the efficient provision of rescue and relief to disaster-affected areas. Since the use of smart
phones is gaining interest in people, the disaster management system was implemented as a smart phone application
using Google’s Android operating system. The disaster management system Android application known as
MyDisasterDroid determines the optimum route along different geographical locations that the volunteers and rescuers
need to take in order to serve the most number of people and provide maximum coverage of the area in the shortest
possible time. Genetic algorithm was applied for optimization and different parameters were varied to determine the
most optimum route.


A report released by the United Nations’ International
Strategy for Disaster Reduction (UNISDR) based on a
new Mortality Rate Index (MRI) ranks the Philippines
no. 12 among 200 countries and territories most at risk
from earthquakes, floods, tropical cyclones, and
landslides [1]. Indeed, through the years, the Philippines
because of its geographic location has been devastated
by several natural disasters. It has claimed the lives of
thousands of people and damaged billions worth of
property. Even with the recurrent calamities, there is still
the absence of an efficient disaster management system
that will help the people in times of calamities.

Disaster Management

The process of disaster management involves four
phases: mitigation, preparedness, response, and
recovery. The mitigation phase is the attempt to reduce
disaster risks by focusing on long-term measures of
eliminating disasters. The preparedness phase is the
development of an action plan for an upcoming disaster.
The response phase is the mobilization of services and
relief when disaster strikes and the recovery phase is the
restoration of the affected area to its previous state [2].

Using Wireless Mobile Technologies

The use of wireless mobile technology in the Philippines
is prevalent. A study showed that one Filipino in two is a
subscriber to a mobile phone service [4] and more than
two billion in the world are mobile users [5]. Because of
the wide coverage of the mobile phone network, it may
be used in the implementation of a disaster management
system making it available in everyone’s pocket.

Smart Phones and Disaster Response

Based on research released by Smartphone Summit,
smart phones account for 10% of all cell phone sales and
it is still growing and driving more interest among
people [7]. One of the reasons for its continued growth
is that it provides information valuable to the users.
Moreover, in times of disaster, the more people that
have information with them all the time, the more they
will be self-reliant allowing rescuers or responders to
concentrate on those in the greatest need of help [8].
There are also a number of mobile applications
available in smart phones that is beneficial in disaster
response. Among these are the GPS technology, which
can be used in the tracking of rescuers and resources, the
translator, which can be used for communication, and
the field examiner, which can be used to send
information to headquarters for assessment of damages
[9]. Indeed, the use of a smart phone in a disaster
management system is advantageous.

Genetic Algorithms

A number of algorithms have been developed to solve
the TSP. Some of which are linear, dynamic, Monte
Carlo, and heuristic search methods. However, results
showed that genetic algorithms produced the lowest
distance solution among the mentioned optimization
methods [12].
Genetic algorithms are computational models
inspired by evolution that provides a potential solution
to a specific problem. It has a wide range of applications
from optimization, test pattern generation, voice
recognition, and image processing. It solves problems by
mimicking the same processes Mother Nature uses.
Usually, when we want to solve a particular problem, we
are looking for some solution, which will be the best
among others. The space of all feasible solutions is
called a search space (state space). Each point in the
search space represents one feasible solution. Each
feasible solution can be “marked” by its value or fitness
for the problem. Genetic algorithm starts with a set of
solutions (represented by chromosomes) called
population. Solutions from one population are taken and
used to form a new population.

MyDisasterDroid Genetic Algorithm

Fig. 2 shows a block diagram of the genetic
algorithm implementation used in MyDisasterDroid.
Based on the figure, after setting the geolocations and
computing the distances between geolocations, initial
solutions or chromosomes are generated. These
chromosomes represent probable routes along the set
geolocations. Then, the fitness of each solution (or
chromosome) were calculated. This is equal to the
summation of the distances between the different
locations based on the order set by the particular
solution. The shorter the distance, the fitter the solution,
and a higher fitness value is achieved. Since the fitness
value is very large, a linear transformation was done to
reduce its value. This is reflected as the score value that
is, a lower score means a higher fitness value.


A disaster management system that facilitates the
logistics for the rescue and relief operations during a
disaster known as MyDisasterDroid was implemented in
an Android- based mobile phone. Geographic locations
of the people in need were sent via SMS or inputted
directly to MyDisasterDroid. Determining the optimum
route along the different geographic locations is similar
to solving the travelling salesman problem wherein the
geographic locations correspond to the cities and the
rescuers or volunteers correspond to the travelling
salesman. Using genetic algorithm, an optimum route
along the given geographic locations was determined.
Different genetic algorithm parameters were varied and
based on the results, the best combination of genetic
algorithm operators to use are the tournament selector
operator with a tournament size of 20 and a swapping
mutation operator with a mutation rate of 20 for 40

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