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Cyber Surveillance for Flood Disasters

Identifieur interne : 000122 ( Pmc/Checkpoint ); précédent : 000121; suivant : 000123

Cyber Surveillance for Flood Disasters

Auteurs : Shi-Wei Lo [Taïwan] ; Jyh-Horng Wu ; Fang-Pang Lin ; Ching-Han Hsu [Taïwan]

Source :

RBID : PMC:4367310

Abstract

Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.


Url:
DOI: 10.3390/s150202369
PubMed: 25621609
PubMed Central: 4367310


Affiliations:


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PMC:4367310

Le document en format XML

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<p>Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.</p>
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</TEI>
<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id>
<journal-id journal-id-type="iso-abbrev">Sensors (Basel)</journal-id>
<journal-title-group>
<journal-title>Sensors (Basel, Switzerland)</journal-title>
</journal-title-group>
<issn pub-type="epub">1424-8220</issn>
<publisher>
<publisher-name>MDPI</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">25621609</article-id>
<article-id pub-id-type="pmc">4367310</article-id>
<article-id pub-id-type="doi">10.3390/s150202369</article-id>
<article-id pub-id-type="publisher-id">sensors-15-02369</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Cyber Surveillance for Flood Disasters</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Lo</surname>
<given-names>Shi-Wei</given-names>
</name>
<xref ref-type="aff" rid="af1-sensors-15-02369">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="af2-sensors-15-02369">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Jyh-Horng</given-names>
</name>
<xref ref-type="aff" rid="af1-sensors-15-02369">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lin</surname>
<given-names>Fang-Pang</given-names>
</name>
<xref ref-type="aff" rid="af1-sensors-15-02369">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Hsu</surname>
<given-names>Ching-Han</given-names>
</name>
<xref ref-type="aff" rid="af2-sensors-15-02369">
<sup>2</sup>
</xref>
<xref rid="c1-sensors-15-02369" ref-type="corresp">
<sup>*</sup>
</xref>
</contrib>
</contrib-group>
<aff id="af1-sensors-15-02369">
<label>1</label>
National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu City 30076, Taiwan; E-Mails:
<email>LSW@nchc.narl.org.tw</email>
(S.-W.L.);
<email>jhwu@nchc.narl.org.tw</email>
(J.-H.W.);
<email>fplin@nchc.narl.org.tw</email>
(F.-P.L.)</aff>
<aff id="af2-sensors-15-02369">
<label>2</label>
Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan</aff>
<author-notes>
<fn id="fn1-sensors-15-02369">
<p>Academic Editor: Albert M. K. Cheng</p>
</fn>
<corresp id="c1-sensors-15-02369">
<label>*</label>
Author to whom correspondence should be addressed; E-Mail:
<email>cghsu@mx.nthu.edu.tw</email>
; Tel.: +886-3-5715131 (ext. 80815).</corresp>
</author-notes>
<pub-date pub-type="collection">
<month>2</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub">
<day>22</day>
<month>1</month>
<year>2015</year>
</pub-date>
<volume>15</volume>
<issue>2</issue>
<fpage>2369</fpage>
<lpage>2387</lpage>
<history>
<date date-type="received">
<day>17</day>
<month>11</month>
<year>2014</year>
</date>
<date date-type="accepted">
<day>12</day>
<month>1</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>© 2015 by the authors; licensee MDPI, Basel, Switzerland.</copyright-statement>
<copyright-year>2015</copyright-year>
<license>
<license-p>This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
).</license-p>
</license>
</permissions>
<abstract>
<p>Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.</p>
</abstract>
<kwd-group>
<kwd>flood</kwd>
<kwd>first warning</kwd>
<kwd>early warning system</kwd>
<kwd>image segmentation</kwd>
<kwd>video surveillance</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group>
<fig id="f1-sensors-15-02369" position="float">
<label>Figure 1.</label>
<caption>
<p>Flowchart of the proposed image-based flood alarm (IFA) system.</p>
</caption>
<graphic xlink:href="sensors-15-02369f1"></graphic>
</fig>
<fig id="f2-sensors-15-02369" position="float">
<label>Figure 2.</label>
<caption>
<p>Example of seed point setup. The figure on the left shows the originally observed image, and the figure on the right shows the set positions of seed points, including both the river water part in the foreground and the surrounding environment (
<italic>i.e.</italic>
, embankment and the sky) in the background. The seed points include two sections: The water region (indicated by red arrows) and the background section to be removed (indicated by the blue arrows).</p>
</caption>
<graphic xlink:href="sensors-15-02369f2"></graphic>
</fig>
<fig id="f3-sensors-15-02369" position="float">
<label>Figure 3.</label>
<caption>
<p>Example of flood region detection. Figure (
<bold>a</bold>
) shows the detection result for the river water region, indicated by the red boundary. In Figure (
<bold>b</bold>
), the white area denotes the flood region, foreground seed points are represented by black plus signs (+), background seed points are represented by yellow “x” symbols, and the user preset warning points are represented by green asterisk symbols (*). In this example, none of the preset warning points are intruded by water; therefore, the risk is 0%.</p>
</caption>
<graphic xlink:href="sensors-15-02369f3"></graphic>
</fig>
<fig id="f4-sensors-15-02369" position="float">
<label>Figure 4.</label>
<caption>
<p>Video screenshots from the water-level detection test. This figure has four best-quality images showing representative overflow changes. From top-left to bottom-right, the acquisition time of the four images is 12:44, 13:25, 15:08, and 16:45, respectively. The content of the experiment is the real video of Typhoon Fanapi taken on 19 September 2010, from 12:00 to 18:00 in the afternoon. The experimental images were captured every 1 min during this period, with a total of 350 images.</p>
</caption>
<graphic xlink:href="sensors-15-02369f4"></graphic>
</fig>
<fig id="f5-sensors-15-02369" position="float">
<label>Figure 5.</label>
<caption>
<p>Test results of a real-life case. Figures (
<bold>a</bold>
) to (
<bold>e</bold>
) show the foreground seed points (+), the background seed points (x), and the preset warning points in green (*) marks in the processed binary images. In these binary images, the white segments indicate the detected water area, while the black segments show the background objects (e.g., ground, the sky, and bridge) to be deleted. Figures (
<bold>f</bold>
) to (
<bold>j</bold>
) show the flooding area detected from the input video, indicated by the red boundary.</p>
</caption>
<graphic xlink:href="sensors-15-02369f5"></graphic>
</fig>
<fig id="f6-sensors-15-02369" position="float">
<label>Figure 6.</label>
<caption>
<p>Online user interface. Appearance of the hydrological monitoring and flood risk warning system in operation. If any monitoring station reaches a risk level of 80%, the video from the on-site camera directly pops up on the interface, with a red outline indicating the near real-time detected flooding area.</p>
</caption>
<graphic xlink:href="sensors-15-02369f6"></graphic>
</fig>
</floats-group>
</pmc>
<affiliations>
<list>
<country>
<li>Taïwan</li>
</country>
</list>
<tree>
<noCountry>
<name sortKey="Lin, Fang Pang" sort="Lin, Fang Pang" uniqKey="Lin F" first="Fang-Pang" last="Lin">Fang-Pang Lin</name>
<name sortKey="Wu, Jyh Horng" sort="Wu, Jyh Horng" uniqKey="Wu J" first="Jyh-Horng" last="Wu">Jyh-Horng Wu</name>
</noCountry>
<country name="Taïwan">
<noRegion>
<name sortKey="Lo, Shi Wei" sort="Lo, Shi Wei" uniqKey="Lo S" first="Shi-Wei" last="Lo">Shi-Wei Lo</name>
</noRegion>
<name sortKey="Hsu, Ching Han" sort="Hsu, Ching Han" uniqKey="Hsu C" first="Ching-Han" last="Hsu">Ching-Han Hsu</name>
</country>
</tree>
</affiliations>
</record>

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