LANDSLIDE SUSCEPTIBILITY MAPPING ALONG NATIONAL HIGHWAY-6, HOA BINH PROVINCE, VIETNAM USING FREQUENCY RATIO MODEL AND GIS

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

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Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand

Environmental Earth Sciences ◽

10.1007/s12665-011-1055-3 ◽

2011 ◽

Vol 64 (8) ◽

pp. 2271-2285 ◽

Cited By ~ 46

Author(s):

N. Intarawichian ◽

S. Dasananda

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Ratio Model ◽

Northern Thailand ◽

Model Based ◽

Frequency Ratio Model

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Landslide Susceptibility Mapping Using GIS-Based Likelihood Frequency Ratio Model: A Case Study of Pakyong—Pacheykhani Area, Sikkim Himalaya

Springer Series in Geomechanics and Geoengineering - Proceedings of International Conference on Remote Sensing for Disaster Management ◽

10.1007/978-3-319-77276-9_51 ◽

2018 ◽

pp. 569-586 ◽

Cited By ~ 1

Author(s):

Satyanarayana Prasad Nerella ◽

Simhachalam Alajangi ◽

Dinesh Dhakal

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Ratio Model ◽

Sikkim Himalaya ◽

Frequency Ratio Model

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Landslide susceptibility mapping using LiDAR derived factors and frequency ratio model: Ulu Klang area, Malaysia

2012 IEEE 8th International Colloquium on Signal Processing and its Applications ◽

10.1109/cspa.2012.6194753 ◽

2012 ◽

Cited By ~ 7

Author(s):

Zulkiflee Abd Latif ◽

Siti Nur Afiqah Aman ◽

Biswajeet Pradhan

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Ratio Model ◽

Frequency Ratio Model

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A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along with Forest Road Networks

Forests ◽

10.3390/f12081087 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1087

Author(s):

Nastaran Zare ◽

Seyed Ata Ollah Hosseini ◽

Mohammad Kazem Hafizi ◽

Akbar Najafi ◽

Baris Majnounian ◽

...

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

Road Networks ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Ratio Model ◽

Forest Road ◽

Neuro Fuzzy ◽

Frequency Ratio Model

In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region.

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