scholarly journals Terrain-based mapping of landslide susceptibility using a geographical information system: a case study

2001 ◽  
Vol 38 (5) ◽  
pp. 911-923 ◽  
Author(s):  
F C Dai ◽  
C F Lee
Keyword(s):  
Information System ◽  
Logistic Regression ◽  
Geographical Information System ◽  
Correspondence Analysis ◽  
Landslide Susceptibility ◽  
Multiple Correspondence Analysis ◽  
Geographical Information ◽  
Slope Aspect ◽  
Outlying Island ◽  
Principal Axes

This paper deals with the development of a technique for mapping landslide susceptibility using a geographical information system (GIS), with particular reference to landslides on natural terrain. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. Landslide susceptibility in the study area is related to a number of terrain variables, viz., lithology, slope gradient, slope aspect, elevation, land cover, and distance to drainage line. Multiple correspondence analysis (MCA) was carried out to generate the principal axes that are linear combinations of these terrain variables using occurrence data of landslides and terrain variables. A GIS is used to project the values of the principal axes, and subsequently to relate these principal axes to landslide susceptibility by logistic regression modeling. The spatial landslide susceptibility response in the study area can then be obtained by applying this logistic regression model to the study area. The results from this study indicate that such a GIS-based model is useful and suitable for the scale adopted in this study.Key words: landslides, geographical information systems, multiple correspondence analysis, logistic regression, terrain analysis.

scholarly journals Geographical Information System (GIS) and Analytical Hierarchy Process (AHP) based groundwater Potential Zones delineation in Chennai River Basin (CRB), India

2021 ◽  
Author(s):  
Sajil Kumar PJ ◽  
Elnago L ◽  
Michael Schneider
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
River Basin ◽  
New Technologies ◽  
Specific Capacity ◽  
Groundwater Potential ◽  
Geographical Information ◽  
Slope Aspect ◽  
Groundwater Depletion ◽  
Groundwater Potential Zones

Abstract Groundwater depletion is one of the most important concerns for users and policy makers. Information on the locations where groundwater potential is high, or low is the key factor that helps them to do proper planning. Application of new technologies and methods are essential in this situation. This study has used the possibilities of Geographical Information System (GIS), Remote Sensing and, of course, field data to delineate the groundwater potential zones in the Chennai River Basin (CRB). To provide accurate results, 11 controlling factors- geology, water level, drainage, soil, lineament, rainfall, land use, slope, aspect, geomorphology, and depth to bed rock-- were brought into a digital GIS environment and appropriate weightage given to each layer depending on their effect on potential. The weightage is given based on Multi-Criteria Decision Making (MCDM), namely Analytical Hierarchal Process (AHP). Groundwater potential zones in the CRB were mapped as very poor, poor, moderate, good, very good using weighted overlay analysis. The results were compared with actual specific capacity from the borehole data. The accuracy of prediction was found to be 78.43%, indicating that in most of the locations, the predicted potential map agrees with the bore hole data. Thus, AHP aided GIS-RS mapping is a useful tool in groundwater prospecting in this region of the world.

scholarly journals A SPATIAL MODEL FOR PREDICTING THE OCCURENCES OF DEFORESTATION IN THE ISLAND OF SUMATRA, INDONESIA

2020 ◽  
Vol 15 (6) ◽  
pp. 75-84
Author(s):  
MUHAMMAD NUR AIDI ◽  
◽  
SANDHI IMAM MAULANA ◽  
Keyword(s):  
Land Use ◽  
Information System ◽  
Logistic Regression ◽  
Geographical Information System ◽  
Soil Type ◽  
Spatial Model ◽  
Geographical Information ◽  
Policy Makers ◽  
Sumatra Island ◽  
Regression Approach

This study primarily aims to develop a spatial model for predicting the occurrences of deforestation in Sumatra Island, Indonesia. This study was conducted based on spatial logistic regression approach that has been widely acknowledged for its flexibility and ability to accept a mixture of both categorical and numerical variables. Result of this study shows that a combination between logistic regression-based modelling and Geographical Information System (GIS) is indeed suitable for determining the probability of deforestation occurrences in Sumatra Island. Analysis conducted in this study has also revealed that physiographic variables, soil type variables, as well as human activity variables have high significant correlation with deforestation. These findings are useful to assist policy makers in Indonesia to understand the process of deforestation and to take it into consideration while formulating land use-related decisions.

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