Peshevski, Igor
Preferred name
Peshevski, Igor
Official Name
Peshevski, Igor
Main Affiliation
Email
pesevski@gf.ukim.edu.mk
54 results
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Item type:Publication, Natural and technological hazards - limiting factor for urban and spatial planning(2023); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Landslide as a risk for the infrastructure and need for national study for geohazards(2022-03); ; Stankovikj, Andrijana - Some of the metrics are blocked by yourconsent settings
Item type:Publication, INFLUENCES OF NATURAL AND MAN – MADE HAZARDS ON ROAD NETWORK AND ELEMENTS OF RISK MANAGEMENT STRATEGY(University Ss. Cyril and Methodius in Skopje, 2020); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Web data mining of landslide information, an experimental study for Macedonia(Macedonian Association for Geotechnics, 2022-06); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Landslide characterization in the Polog Region (R.N. Macedonia) by innovative and conventional methods(2021); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ВОДОПРОПУСНОСТ НА ХИДРОЈАЛОВИШТЕН ПЕСОК: НАОДИ ОД ИСПИТУВАЊА И ИНОВИРАЊЕ НА РЕЛАЦИИ(Здружение Македонски Комитет за Високи Брани, 2021-09); ; Nikolikj, Marija - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Landslide hazard factors in the Kozuf metallogenic district(GIS-GEOINDUSTRY, s.r.o. Prague, 2014); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Web data mining of landslide information, an experimental study for Macedonia(ISRM, 2022-06-23); The current paper introduces an innovative method to generate a landslide database utilising the possibilities offered by web data mining techniques. As sources of data are used social media networks and news aggregators, in combination with already available landslide databases of the authors. The algorithm of the data mining is explained, as well as the rules for relevant classification and recognition of landslide information. The territory of Macedonia is taken as a case study to test the method. Findings show that the approach can be very useful from various aspects, with main advantage being the swift landslide data collection and possibility for sharing among relevant institutions. The approach is also considered promising in regard to timely alarming of the population for expected danger from landslides. Certain aspects of possibilities for mining additional web content related to landslides are noted, as well as options for integration of the web mined landslide data with already existing databases and sharing among entities. Due to the advantage of language recognition during the data mining, the utilisation of the presented method is possible on both national and international level.