{"version":"1.0","provider_name":"\u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","provider_url":"http:\/\/www.geology.com.ua\/en","author_name":"\u0410\u0434\u043c\u0456\u043d\u0456\u0441\u0442\u0440\u0430\u0442\u043e\u0440","author_url":"http:\/\/www.geology.com.ua\/en\/blog\/author\/andriy\/","title":"Geoinformatika 2016; 2(58) : 79-85 - \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"FXAuRq7DTY\"><a href=\"http:\/\/www.geology.com.ua\/en\/geoinformatika-2016-258-79-85\/\">Geoinformatika 2016; 2(58) : 79-85<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"http:\/\/www.geology.com.ua\/en\/geoinformatika-2016-258-79-85\/embed\/#?secret=FXAuRq7DTY\" width=\"600\" height=\"338\" title=\"&#8220;Geoinformatika 2016; 2(58) : 79-85&#8221; &#8212; \u0421\u0430\u0439\u0442 \u0436\u0443\u0440\u043d\u0430\u043b\u0443 \u00ab\u0413\u0435\u043e\u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u043a\u0430\u00bb\" data-secret=\"FXAuRq7DTY\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=http:\/\/www.geology.com.ua\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"Geoinformatika 2016; 2(58) : 79-85 (in Ukrainian) METHODOLOGY OF QUANTITATIVE RISK ASSESSMENT DUE TO DEVELOPMENT OF EXOGENOUS GEOLOGICAL PROCESSES: MUDFLOWS RISKS T.B. Chepurna, D.V. Kasiyanchuk, E.D. Kuzmenko, I.V. Chepurnyj Ivano-Frankivsk National Technical University of Oil and Gas, 15 Karpatska Str., Ivano-Frankivsk 76000, Ukraine, e-mail: gbg@nung.edu.ua The purpose of the study is to develop a quantitative methodology to predict risk of EGP based on prognostic spatial and temporal patterns of their individual types (village, karst, landslides, flooding), which are built taking into account the combined effect of initiating factors. Testing the proposed methodology was implemented by constructing a cartographic model for assessing risks to mud of the Eastern part of the Upper Tysa basin. Design\/methodology\/approach. The experimental investigations were based on the development of an algorithm of quantitative forecasting risk of EGP showing a hierarchically structured methodology stages of a quantitative predictive risk assessment, on applying the algorithm to predict mud risks, on mathematically presenting mudflow risk considering the probabilities of the spatial distribution of mud cells and temporal dynamics of their manifestation. The algorithm was tested, by constructing a debris risk assessment model. Findings. The results showed the need to develop a new approach to environmental and geological risk assessment of integral exogenous processes, which would take into account complex factors influencing their development. The methodology proposed by the authors is a logical extension of the research on space-time forecasting of EGP and is based on calculating complex spatial and temporal indicators. The algorithm of the ecological and geological assessment of the integrated EPG risk includes seven main stages: identification of EGP, for which risks are to be calculated; space-time analysis of complex EGP considering initiating factors; creation of prediction models of space-time development of EGP; selection of a spatial and analytical-descriptive system evaluate risks and dangers of their manifestations; construction of prediction risk assessments maps by EPG type; generalization of assessments of space protection systems and construction of integrated risk maps for all EGP types. Practical value\/implications. The method of calculating the collective risk for any area allows for high spatial and temporal probability of mud flows, the share of the total area of mud alluvial fans over an analyzed period in an area (region) as a measure of maximum planar destruction, population density, and availability of protective systems on site. The authors have built a map of mudflow risks to be used by administrative-territorial units (town and city councils). The mudflow risks are calculated for a\u00a0 year of maximum mudflow activity. Keywords: exogenous geological processes, mud risks, factor, assessment, cartogram. The full text of papers"}