<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462023000401157</article-id>
<article-id pub-id-type="doi">10.13053/cys-27-4-4643</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Flood Prediction with Optimized Attributes and Clustering]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nanda]]></surname>
<given-names><![CDATA[Sarmistha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Panigrahi]]></surname>
<given-names><![CDATA[Chhabi Rani]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pati]]></surname>
<given-names><![CDATA[Bibudhendu]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Rama Devi Women&#8217;s University  ]]></institution>
<addr-line><![CDATA[Bhubaneswar ]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>27</volume>
<numero>4</numero>
<fpage>1157</fpage>
<lpage>1167</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462023000401157&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462023000401157&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462023000401157&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: An emergency is a situation that poses an immediate risk to health, life, property, or environment. Most emergencies require urgent intervention to prevent a worsening of the situation. So, it is always better to predict the emergency before its happening and to take action for optimizing the loss. In this work, we tried to predict the flood by analysing the month-wise rainfall index of a particular area. First, we tried to find the months which have more contributions towards predicting the flood. For this, we used Particle Swarm Optimization (PSO) as feature selection technique and then applied classification algorithms such as J48 and Random Forest (RF). The experimentation was done for both without and with feature selection on the considered dataset. The results obtained without feature selection indicate that 70.34% and 78.81% of data are correctly classified and with feature selection 66.10% and 76.27% respectively with J48 and RF classifiers. Then we removed the class attribute from the dataset to see the effect of results when the class is not available and we applied K-means and Density Based clustering techniques on the same dataset. It was observed from the results that K-means with manhattan distance approach and Density Based clustering without feature selection classifies accurately 72.03% and 72.88% of data respectively. Similarly, when K-means and Density Based clustering were used with feature selection, it was found that K-means and Distanced Based clustering result in correct classification of 70.03% and 68.64% of data. We had also compared the model building time for both classification and clustering techniques using without and with feature selection. It was noticed that although the accuracy percentage was decreased with feature selection in both the cases. However, the model building time was reduced by 29%, 50%, 78%, and 60% in case of j48, RF, K-means, and Density Based techniques respectively.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Feature selection]]></kwd>
<kwd lng="en"><![CDATA[PSO]]></kwd>
<kwd lng="en"><![CDATA[clustering techniques]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[Manhattan distance]]></kwd>
<kwd lng="en"><![CDATA[emergency]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="">
<collab>United Nations</collab>
<source><![CDATA[Data application of the month: Machine Learning for Flood Detection]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="book">
<collab>Mukul</collab>
<source><![CDATA[Flood prediction model]]></source>
<year>2023</year>
<publisher-name><![CDATA[Kaggle]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Holmes]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Donkin]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Witten]]></surname>
<given-names><![CDATA[I. H.]]></given-names>
</name>
</person-group>
<source><![CDATA[Weka: A machine learning workbench]]></source>
<year>1994</year>
<conf-name><![CDATA[ ANZIIS'94-Australian New Zealnd Intelligent Information Systems Conference]]></conf-name>
<conf-loc> </conf-loc>
<page-range>357-61</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fong]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Biuk-Aghai]]></surname>
<given-names><![CDATA[R. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Millham]]></surname>
<given-names><![CDATA[R. C.]]></given-names>
</name>
</person-group>
<source><![CDATA[Swarm search methods in weka for data mining]]></source>
<year>2018</year>
<conf-name><![CDATA[ 10th International Conference on Machine Learning and Computing]]></conf-name>
<conf-date>2018</conf-date>
<conf-loc> </conf-loc>
<page-range>122-7</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cai]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Luo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feature selection in machine learning: A new perspective]]></article-title>
<source><![CDATA[Neurocomputing]]></source>
<year>2018</year>
<volume>300</volume>
<page-range>70-9</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yadav]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Sharma]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review of K-mean algorithm]]></article-title>
<source><![CDATA[International Journal of Engineering Trends and Technology]]></source>
<year>2013</year>
<volume>4</volume>
<numero>7</numero>
<issue>7</issue>
<page-range>2972-6</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kennedy]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Eberhart]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[Particle swarm optimization]]></source>
<year>1995</year>
<volume>4</volume>
<conf-name><![CDATA[ ICNN'95-international conference on neural networks]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1942-8</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Siegrist]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Gutscher]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Natural hazards and motivation for mitigation behavior: People cannot predict the affect evoked by a severe flood. Risk Analysis]]></article-title>
<source><![CDATA[An International Journal]]></source>
<year>2008</year>
<volume>28</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>771-8</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Siegrist]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Cvetkovich]]></surname>
<given-names><![CDATA[G. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Gutscher]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Shared values, social trust, and the perception of geographic cancer clusters]]></article-title>
<source><![CDATA[Risk Analysis]]></source>
<year>2001</year>
<volume>21</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1047-54</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jaberipour]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Khorram]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Karimi]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Particle swarm algorithm for solving systems of nonlinear equations]]></article-title>
<source><![CDATA[Computers &amp; Mathematics with Applications]]></source>
<year>2011</year>
<volume>62</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>566-76</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lawal]]></surname>
<given-names><![CDATA[Z. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Yassin]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zakari]]></surname>
<given-names><![CDATA[R. Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Flood prediction using machine learning models: a case study of Kebbi state Nigeria]]></source>
<year>2021</year>
<conf-name><![CDATA[ IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-6</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Syeed]]></surname>
<given-names><![CDATA[M. M. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Farzana]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Namir]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Ishrar]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Nushra]]></surname>
<given-names><![CDATA[M. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Rahman]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<source><![CDATA[Flood prediction using machine learning models]]></source>
<year>2022</year>
<conf-name><![CDATA[ International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-6</page-range><publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zehra]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction analysis of floods using machine learning algorithms (NARX &amp; SVM)]]></article-title>
<source><![CDATA[International Journal of Sciences: Basic and Applied Research (IJSBAR)]]></source>
<year>2020</year>
<volume>49</volume>
<numero>2</numero>
<issue>2</issue>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kanwar]]></surname>
<given-names><![CDATA[B. P. S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Development of flood prediction models using machine learning techniques]]></source>
<year>2022</year>
<publisher-name><![CDATA[Missouri University of Science and Technology]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maspo]]></surname>
<given-names><![CDATA[N. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Harun]]></surname>
<given-names><![CDATA[A. N. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Goto]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheros]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Haron]]></surname>
<given-names><![CDATA[N. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Nawi]]></surname>
<given-names><![CDATA[M. N. M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Evaluation of machine learning approach in flood prediction scenarios and its input parameters: A systematic review]]></source>
<year>2020</year>
<volume>479</volume>
<numero>1</numero>
<conf-name><![CDATA[ IOP Conference Series: Earth and Environmental Science]]></conf-name>
<conf-loc> </conf-loc>
<issue>1</issue>
<page-range>012038</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kotsiantis]]></surname>
<given-names><![CDATA[S. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Kanellopoulos]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Pintelas]]></surname>
<given-names><![CDATA[P. E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Data preprocessing for supervised leaning]]></article-title>
<source><![CDATA[International journal of computer science]]></source>
<year>2006</year>
<volume>1</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>111-7</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alshdaifat]]></surname>
<given-names><![CDATA[E. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Alshdaifat]]></surname>
<given-names><![CDATA[D. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Alsarhan]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Hussein]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[El-Salhi]]></surname>
<given-names><![CDATA[S. M. D. F. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The effect of preprocessing techniques, applied to numeric features, on classification algorithms&#8217; performance]]></article-title>
<source><![CDATA[Data]]></source>
<year>2021</year>
<volume>6</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>11</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Na]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Xumin]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Yong]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<source><![CDATA[Research on k-means clustering algorithm: An improved k-means clustering algorithm]]></source>
<year>2010</year>
<conf-name><![CDATA[ Third International Symposium on intelligent information technology and security informatics]]></conf-name>
<conf-loc> </conf-loc>
<page-range>63-7</page-range></nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ahmed]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Seraj]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Islam]]></surname>
<given-names><![CDATA[S. M. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The k-means algorithm: A comprehensive survey and performance evaluation]]></article-title>
<source><![CDATA[Electronics]]></source>
<year>2020</year>
<volume>9</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>295</page-range></nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Oyelade]]></surname>
<given-names><![CDATA[O. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Oladipupo]]></surname>
<given-names><![CDATA[O. O.]]></given-names>
</name>
<name>
<surname><![CDATA[Obagbuwa]]></surname>
<given-names><![CDATA[I. C.]]></given-names>
</name>
</person-group>
<source><![CDATA[Application of k-means clustering algorithm for prediction of students academic performance]]></source>
<year>2010</year>
</nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Szabo]]></surname>
<given-names><![CDATA[F. E.]]></given-names>
</name>
</person-group>
<source><![CDATA[Manhattan distance]]></source>
<year>2015</year>
<publisher-name><![CDATA[ScienceDirect]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Suwanda]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Syahputra]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zamzami]]></surname>
<given-names><![CDATA[E. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Analysis of euclidean distance and manhattan distance in the K-means algorithm for variations number of centroid K]]></article-title>
<source><![CDATA[Journal of Physics: Conference Series IOP Publishing]]></source>
<year>2020</year>
<volume>1566</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[K. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An efficient and scalable density-based clustering algorithm for normalize data]]></article-title>
<source><![CDATA[Procedia Computer Science]]></source>
<year>2016</year>
<volume>92</volume>
<page-range>136-41</page-range></nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bhattacharjee]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Mitra]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey of density based clustering algorithms]]></article-title>
<source><![CDATA[Frontiers of Computer Science]]></source>
<year>2021</year>
<volume>15</volume>
<page-range>1-27</page-range></nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="">
<collab>ArcGIS Pro</collab>
<source><![CDATA[How density-based clusterind works]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kulkarni]]></surname>
<given-names><![CDATA[V. Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Sinha]]></surname>
<given-names><![CDATA[P. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Random forest classifiers: a survey and future research directions]]></article-title>
<source><![CDATA[International Journal of Advanced Computing]]></source>
<year>2013</year>
<volume>36</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1144-53</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alazawi]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Abdljabar]]></surname>
<given-names><![CDATA[M. B.]]></given-names>
</name>
<name>
<surname><![CDATA[Altowaijri]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Vegni]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Mehmood]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[ICDMS: an intelligent cloud based disaster management system for vehicular networks]]></source>
<year>2012</year>
<volume>7266</volume>
<conf-name><![CDATA[ Communication Technologies for Vehicles: 4th International Workshop, Nets4Cars/Nets4 Trains]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nanda]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Panigrahi]]></surname>
<given-names><![CDATA[C. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pati]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<source><![CDATA[An architectural framework to manage heterogeneous emergencies]]></source>
<year>2022</year>
<volume>431</volume>
<conf-name><![CDATA[ Intelligent Systems: Proceedings of ICMIB]]></conf-name>
<conf-date>2021</conf-date>
<conf-loc>Singapore </conf-loc>
<page-range>169-77</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models]]></article-title>
<source><![CDATA[Journal of environmental management]]></source>
<year>2021</year>
<volume>293</volume>
</nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jacinth]]></surname>
<given-names><![CDATA[J. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Saravanan]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Abijith]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Integration of SAR and multi-spectral imagery in flood inundation mapping&#8211;a case study on Kerala floods 2018]]></article-title>
<source><![CDATA[ISH Journal of Hydraulic Engineering]]></source>
<year>2022</year>
<volume>28</volume>
<page-range>480-90</page-range></nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ravansalar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rajaee]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Kisi]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Wavelet-linear genetic programming: a new approach for modeling monthly streamflow]]></article-title>
<source><![CDATA[Journal of Hydrology]]></source>
<year>2017</year>
<volume>549</volume>
<page-range>461-75</page-range></nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gizaw]]></surname>
<given-names><![CDATA[M. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Gan]]></surname>
<given-names><![CDATA[T. Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Regional flood frequency analysis using support vector regression under historical and future climate]]></article-title>
<source><![CDATA[Journal of Hydrology]]></source>
<year>2016</year>
<volume>538</volume>
<page-range>387-98</page-range></nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lohani]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Goel]]></surname>
<given-names><![CDATA[N. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Bhatia]]></surname>
<given-names><![CDATA[K. K. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Improving real time flood forecasting using fuzzy inference system]]></article-title>
<source><![CDATA[Journal of hydrology]]></source>
<year>2014</year>
<volume>509</volume>
<page-range>25-41</page-range></nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Damle]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Yalcin]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Flood prediction using time series data mining]]></article-title>
<source><![CDATA[Journal of Hydrology]]></source>
<year>2007</year>
<volume>333</volume>
<numero>2-4</numero>
<issue>2-4</issue>
<page-range>305-16</page-range></nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mosavi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ozturk]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Chau]]></surname>
<given-names><![CDATA[K. W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Flood prediction using machine learning models: Literature review]]></article-title>
<source><![CDATA[Water]]></source>
<year>2018</year>
<volume>10</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>1536</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
