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		<id>https://www.scipedia.com/wd/index.php?action=history&amp;feed=atom&amp;title=Habutsu_et_al_2024a</id>
		<title>Habutsu et al 2024a - Revision history</title>
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		<updated>2026-05-05T14:50:02Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305636&amp;oldid=prev</id>
		<title>JSanchez: JSanchez moved page Draft Sanchez Pinedo 730367406 to Habutsu et al 2024a</title>
		<link rel="alternate" type="text/html" href="https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305636&amp;oldid=prev"/>
				<updated>2024-07-01T11:26:04Z</updated>
		
		<summary type="html">&lt;p&gt;JSanchez moved page &lt;a href=&quot;/public/Draft_Sanchez_Pinedo_730367406&quot; class=&quot;mw-redirect&quot; title=&quot;Draft Sanchez Pinedo 730367406&quot;&gt;Draft Sanchez Pinedo 730367406&lt;/a&gt; to &lt;a href=&quot;/public/Habutsu_et_al_2024a&quot; title=&quot;Habutsu et al 2024a&quot;&gt;Habutsu et al 2024a&lt;/a&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
				&lt;tr style='vertical-align: top;' lang='en'&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 11:26, 1 July 2024&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan='2' style='text-align: center;' lang='en'&gt;&lt;div class=&quot;mw-diff-empty&quot;&gt;(No difference)&lt;/div&gt;
&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305635&amp;oldid=prev</id>
		<title>JSanchez at 11:25, 1 July 2024</title>
		<link rel="alternate" type="text/html" href="https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305635&amp;oldid=prev"/>
				<updated>2024-07-01T11:25:57Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;col class='diff-marker' /&gt;
				&lt;col class='diff-content' /&gt;
				&lt;tr style='vertical-align: top;' lang='en'&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 11:25, 1 July 2024&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l3&quot; &gt;Line 3:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 3:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;This paper presents a development of land use classification model based on semantic segmentation using aerial photographs and its contribution to the efficiency of 2D tsunami inundation simulations. The proposed method uses Artificial Intelligence(AI)-based image classification to generate a roughness coefficient mesh, which is then applied to a 2-D tsunami run-up simulation using the finite element method on real geometry. Numerical results are compared to evaluate the improvement in simulation efficiency, and the potential benefits of the proposed method are discussed by analyzing the differences in simulation results.&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;This paper presents a development of land use classification model based on semantic segmentation using aerial photographs and its contribution to the efficiency of 2D tsunami inundation simulations. The proposed method uses Artificial Intelligence(AI)-based image classification to generate a roughness coefficient mesh, which is then applied to a 2-D tsunami run-up simulation using the finite element method on real geometry. Numerical results are compared to evaluate the improvement in simulation efficiency, and the potential benefits of the proposed method are discussed by analyzing the differences in simulation results.&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;== Full Paper ==&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;lt;pdf&amp;gt;Media:Draft_Sanchez Pinedo_730367406107.pdf&amp;lt;/pdf&amp;gt;&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305633&amp;oldid=prev</id>
		<title>JSanchez at 11:25, 1 July 2024</title>
		<link rel="alternate" type="text/html" href="https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305633&amp;oldid=prev"/>
				<updated>2024-07-01T11:25:55Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
				&lt;col class='diff-marker' /&gt;
				&lt;col class='diff-content' /&gt;
				&lt;col class='diff-marker' /&gt;
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				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 11:25, 1 July 2024&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l1&quot; &gt;Line 1:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 1:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;==Abstract==&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td colspan=&quot;2&quot;&gt;&amp;#160;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;This paper presents a development of land use classification model based on semantic segmentation using aerial photographs and its contribution to the efficiency of 2D tsunami inundation simulations. The proposed method uses Artificial Intelligence(AI)-based image classification to generate a roughness coefficient mesh, which is then applied to a 2-D tsunami run-up simulation using the finite element method on real geometry. Numerical results are compared to evaluate the improvement in simulation efficiency, and the potential benefits of the proposed method are discussed by analyzing the differences in simulation results.&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305632&amp;oldid=prev</id>
		<title>JSanchez: Created blank page</title>
		<link rel="alternate" type="text/html" href="https://www.scipedia.com/wd/index.php?title=Habutsu_et_al_2024a&amp;diff=305632&amp;oldid=prev"/>
				<updated>2024-07-01T11:25:54Z</updated>
		
		<summary type="html">&lt;p&gt;Created blank page&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;/div&gt;</summary>
		<author><name>JSanchez</name></author>	</entry>

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