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Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chronométrie [Termes IGN] données spatiotemporelles [Termes IGN] gestion de crise [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle de simulation [Termes IGN] plan de prévention des risques [Termes IGN] questionnaire [Termes IGN] risque naturel [Termes IGN] secours d'urgence Résumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986 [article]

Tsunami inundation modelling for the coast of Kerala, India / S. Praveen in Marine geodesy, vol 36 n° 1 (January - March 2013)  

Public [article]  inMarine geodesy > vol 36 n° 1 (January - March 2013) . - pp 86 - 97 Titre : Tsunami inundation modelling for the coast of Kerala, India Type de document : Article/Communication Auteurs : S. Praveen, Auteur ; N. Kurian, Auteur ; T. Hameed, Auteur Année de publication : 2013 Article en page(s) : pp 86 - 97 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle numérique bathymétrique [Termes IGN] montée du niveau de la mer [Termes IGN] niveau de la mer [Termes IGN] tsunami Résumé : (Auteur) The disastrous tsunami of December 26, 2004, exposed the urgent need for implementing a tsunami warning system. One of the essential requirements of a tsunami warning system is the set up of tsunami inundation models which can predict inundation and run-up along a coastline for a given set of seismic parameters. The Tsunami Warning Centre and the State/District level Disaster Management Centres should have tsunami inundations maps for different scenarios of tsunami generation. In the event of a tsunamigenic earthquake, appropriate decisions on issue of warnings and/or evacuation of coastal population are made by referring to such maps. The nature of tsunami inundation and run-up along the Kerala coast for the 2004 Sumatra and 1945 Makran, and a hypothetical worst-case scenario are simulated using the TUNAMI N2 model and the results are presented in this paper. Further, scenarios of tsunami inundation arising out of possible rise in sea level as projected by the Intergovernmental Panel on Climate Change (IPCC 2001) are also simulated and analysed in the paper. For the study, three representative sectors of the Kerala coast including the Neendakara-Kayamkulam coast, which was the worst hit by the 2004 tsunami, are chosen. The results show that the southern locations and certain locations of central Kerala coast are more vulnerable for Sumatra when compared to Makran 1945 tsunami. From the results of numerical modelling for future scenarios it can be concluded that sea level rise can definitely make pronounced increase in inundation in some of the stretches where the backshore elevation is comparatively low. Numéro de notice : A2013-247 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2012.699502 Date de publication en ligne : 13/03/2013 En ligne : https://doi.org/10.1080/01490419.2012.699502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32385 [article]

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Geographic information system–based morphometric characterization of sub-watersheds of Meenachil river basin, Kottayam district, Kerala, India / M. Vincy in Geocarto international, vol 27 n° 8 (December 2012)  

Public [article]  inGeocarto international > vol 27 n° 8 (December 2012) . - pp 661 - 684 Titre : Geographic information system–based morphometric characterization of sub-watersheds of Meenachil river basin, Kottayam district, Kerala, India Type de document : Article/Communication Auteurs : M. Vincy, Auteur ; B. Rajan, Auteur ; A. Pradeepkumar, Auteur Année de publication : 2012 Article en page(s) : pp 661 - 684 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] ArcGIS [Termes IGN] bassin hydrographique [Termes IGN] caractérisation [Termes IGN] géomorphométrie [Termes IGN] Kerala (Inde ; état) Résumé : (Auteur) The study areas Tikovil and Payppara sub-watersheds of Meenachil river cover 158.9 and 111.9 km2, respectively. These watersheds are parts of Western Ghats, which is an ecologically sensitive region. The drainage network of the sub-watersheds was delineated using SOI topographical maps on 1:50,000 scale using the Arc GIS software. The stream orders were calculated using the method proposed by Strahler's (1964). The drainage network shows that the terrain exhibits dendritic to sub-dendritic drainage pattern. Stream order ranges from the fifth to the sixth order. Drainage density varies between 1.69 and 2.62 km/km2. The drainage texture of the drainage basins are 2.3 km–1 and 6.98 km–1 and categorized as coarse to very fine texture. Stream frequency is low in the case of Payappara sub-watershed (1.78 km–2). Payappara sub-watershed has the highest constant of channel maintenance value of 0.59 indicating much fewer structural disturbances and fewer runoff conditions. The form factor value varies in between 0.42 and 0.55 suggesting elongated shape formed for Payappara sub-watershed and a rather more circular shape for Tikovil sub-watershed. The mean bifurcation ratio (3.5) indicates that both the sub-watersheds are within the natural stream system. Hence from the study it can be concluded that GIS techniques prove to be a competent tool in morphometric analysis. Numéro de notice : A2012-552 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.657694 Date de publication en ligne : 15/02/2012 En ligne : https://doi.org/10.1080/10106049.2012.657694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31998 [article]

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