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首页印度泰米尔纳德邦灾害恢复:基于数字海拔预测安全路线
印度泰米尔纳德邦灾害恢复:基于数字海拔预测安全路线
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本文档探讨了通过数字高程模型预测灾难中的安全路线以实现灾难恢复的方法。"DISASTER RECOVERY THROUGH PREDICTION OF SAFER ROUTE USING DIGITAL ELEVATION MODELS"是一篇发表在《智能计算与生物信息学进展》(Advances in Smart Computing and Bioinformatics)特刊上的文章,重点针对四月期。研究由Sangavi Vivp Mounikan和Graceeline Jasminen两位作者完成,他们来自印度VIT大学的软件工程系。 在自然灾害发生时,传统的交通路线常常中断,导致人们被困于灾难区域,无法正常通信或找到撤离至安全地带的路径。本文的主要目标是开发一个原型系统,利用ArcGIS地理信息系统(ArcGIS)的Java应用程序运行时SDK和Eclipse中的API,专注于印度地图,特别是泰米尔纳德邦的地形数据。系统的核心是数字高程模型层,它能根据受灾区域到安全地点的海拔值来确定最安全的路线。 系统的优势在于,救援机构可以利用这个工具迅速定位灾害现场,并规划出一条相对平坦、风险较低的路径,以便有效地疏散受灾人员。这有助于提高救援行动的效率和安全性,减少因道路状况带来的额外危险。这项研究旨在提供一种创新的灾难应对策略,为紧急情况下的实时决策支持提供科学依据。通过集成地理信息和数字地形分析,它具有显著的实际应用价值,尤其是在复杂地理环境和频繁自然灾害频发的地区。
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Special Issue (April)
Online - 2455-3891
Print - 0974-2441
Advances in Smart Computing and Bioinformatics
DISASTER RECOVERY THROUGH PREDICTION OF SAFE ROUTE USING DIGITAL ELEVATION
MODEL LEVELS
SANGAVI VP
1
MOUNIKA N
1
, GRACELINE JASMINE N
2
1
Department of Software Engineering, School of Computing Science and Engineering, VIT University, Chennai, Tamil Nadu, India.
ABSTRACT
When a disaster occurs, the normal commutation routes are disrupted. People get stuck at these disaster points and would be in trouble, hence people
in those areas find it difficult to communicate and evacuation route to safe area is unknown. The aim of the paper is to predict safe routes to reach the
refuge point from the disaster point. The prototype was developed using Arc geographic information system runtime SDK for Java Application and
APIs in Eclipse. The system was developed with digital elevation model layer, and route layer for India basemap focused to Tamil Nadu. The safe route
is found based on the elevation values of the area from the disaster point to a safe point. The developed system could be used by the relief providers
to reach the disaster point and rescue victims.
Keywords: Digital elevation model, Disaster, Elevation levels, Environmental Systems Research Institute, Geographic information system,
Threshold value.
INTRODUCTION
Natural disaster is an action occurs due to the natural calamities happens
across the globe. Natural disasters may lead to life loss and property
damage depending on its intensity levels. Any natural disaster is often
beyond repair or takes a lot of time to revert back. Natural disasters,
worldwide have become more intense in the recent years. The frequency
of earthquakes, Heat waves, and mega storms has gone up in the last
few decades. Areas that get hit by floods, cyclones, and hurricanes
have encountered more life loss. In some areas, as disaster mitigation
activities people engineered shelters to protect themselves against
these disasters. However, a certain percent of loss of property is still a
problem, and predicting these natural events are difficult. Scientists and
geologists work to predict major disasters and avert as much damage
as possible. With all the technology available, it has become easier to
predict major weather-related natural disasters. However, there are
still natural disasters that come rather unexpectedly. Areas that are not
used to disasters, affected by flash floods or sudden hailstorms will be
affected in an extreme way. There are several organizations set up with
the primary goal for providing aid to the victims. These organizations
work on global and local scale rescue work. Those who have chosen to
make disaster relief their life-work, when disasters hit can be provided
with technology to assist rescue activities. Our project can aid as rescue
support to victims and organizations to face the disaster.
EXISTING METHODOLOGIES
The research toward climatic hazard management has been addressed
by geographic information scientist over a period of time. In this section,
various such methods were discussed. In Forkuo’s study [10], a composite
flood hazard index (FHI) was used by considering the properties such as
near distance, population, number of towns in each district, wetland and
high ground area. This has been formulated as given below:
FHI = (near distance + population + N_Towns + Wetland_Area + R_shelter)
(1)
In Sanyal and Lu’s study [11], the identification of higher hazard zone
was used as a parameter. In due course of time, geographic information
system (GIS) techniques were combined with the technology of
remote sensing to provide more accuracy in the prediction. In the
papers [12,13], many approaches for creating flood hazard maps are
discussed. In all these papers, initial level of the solution is given to
manage various problems which can be caused by flood using GIS and
remote sensing.
PROPOSED SYSTEM
There are apps and prediction systems available to track the disaster
points and inform the user. There are several disaster prediction
systems and alert systems that are used to shift the people before
disaster. There are apps providing details about the disaster intensity,
date, time. However, the situation changes after the disaster. The
geographic pattern after the disaster is different from the geographic
pattern before the disaster. The geographic data after the disaster are
different from before disaster. Hence, it becomes difficult to know the
intensity of damage occurred in the area. The proposed system uses
services provided by Environmental Systems Research Institute (ESRI),
California. ArcGIS a tool provided by ESRI was used in developing the
project. ArcGIS provides functionalities to work on maps. It enables
us to add maps from ArcGIS online services to the current workspace
and enables us to manipulate data in it. The project solely depends
on digital elevation model (DEM) is layer of map that can be added
to the basemap. It gives the elevation values of the land area from
the sea level. To get the elevation values, spatial analysis is done and
displayed as point features. We have marked certain points on the
map and spatial analysis tool extracts the DEM levels for the points
marked on the map. At the final stage route analysis extension is used
to find the route between the given points. Route analysis finds the
quickest or shortest route depending on the impedance such as time
attribute with historic or current traffic data. For our system indirectly,
the impedance attribute is DEM levels. To perform route analysis, we
need a basemap view called as street view. This would provide the path
details for the route analyst. To perform the functionalities provided
by the above-mentioned extensions, we need to store our maps and
data in the form of a map package. Our system analyses the before
disaster scenario and predicts the safe areas. This system is developed
on the world street map as its basemap, retrieved from ESRI online
© 2017 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons.
org/licenses/by/4. 0/) DOI: http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19539
2
Department of Software Engineering, School of Computing Science and Engineering, VIT University, Chennai, Tamil Nadu, India. Email:
asha.s@vit.ac.in
Received: 23 January 2017, Revised and Accepted: 03 March 2017
Full Proceeding Paper
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