Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: a case study for landslide hazard zonation

2011 ◽  
Author(s):  
Pai-Hui Hsu ◽  
Wen-Ray Su ◽  
Chy-Chang Chang
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation

scholarly journals LANDSLIDE HAZARD ZONATION MAPPING OF CHAMOLI LANDSLIDES IN REMOTE SENSING AND GIS ENVIRONMENT

2018 ◽  
Vol XLII-5 ◽  
pp. 475-480
Author(s):  
M. K. Tripathi ◽  
H. Govil ◽  
P. K. Champati ray ◽  
I. C. Das
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Information Value ◽  
Slope Aspect ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Thematic Layers ◽  
Zonation Map ◽  
Gis Environment

<p><strong>Abstract.</strong> Landslides are very common problem in hilly terrain. Chamoli region of Himalaya is highest sensitive zone of the landslide hazards. The purpose of Chamoli landslide study, to observe the important terrain factors and parameters responsible for landslide initiation. Lithological, geomorphological, slope, aspect, landslide, drainage density and lineament density map generated in remote sensing and GIS environment. Data information of related geological terrain obtain through topographic maps, remote sensing images, field visits and geological maps. Geodatabases of all thematic layers prepared through digitization of topographic map and satellite imageries (LISS-III, LISS-IV &amp;amp; ASTER DEM). Integrated all thematic layers applying information value method under GIS environment to map the zonation of landslide hazard zonation map validation and verification completed by field visit. The landslide hazard zonation map classified in four classes very high, high, medium and low.</p>

scholarly journals An Empirical Research on Spatial Data Mining

2019 ◽  
Vol 8 (12S2) ◽  
pp. 797-800
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Social Networks ◽  
Knowledge Discovery ◽  
Empirical Research ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Information Discovery ◽  
Promising Area

Spatial data mining is a process of extracting expertise from large volumes of spatial data collected from different applications such as remote sensing, geographic systems and social networks, etc. The collected spatial data are too difficult for the human to analyze. Recent research focuses on data mining to extend the data mining scope from relational storages to spatial databases. A lot of effort put forth to summarize various spatial based knowledge discovery in data mining such as based on generalization, clustering based, spatial associations based, and approximations and aggregations based knowledge discovery are discussed. The discussion shows that spatial data mining is a promising area of information discovery and can lead to extensive research and many challenging issues.

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