Representing Landslides as Polygon (Areal) or Points? How Different Data Types Influence the Accuracy of Landslide Susceptibility Maps

Abstract

In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon) applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets both representing the same landslides within the study area. These datasets were later separated into training and validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will influence the accuracy of the final landslide potential map.

Landslide Factors and Susceptibility Mapping on Natural and Artificial Slopes in Kundasang, Sabah

“…The landslide inventory is used to store the data on the landslides, in precise the location and areal extent of each landslide (Simon et al 2017). A total of 178 aerial photographs were used to map landslides with the scale of 1:12 000 throughout three assessment years, starting from 1984, 2009 and 2012.…”

Morphological parameters causing landslides: a case study of elevation

“…Pourghasemi et al, 2014 <1,500, 1,500-2,000, 2,000-2,500, 2,500-3,000, 3,000-3,500, >3,500 500 Pourtaghi et al, 2014<100, 100-500, 500-1000, 1000-1500, 1500-2000, 2000-2500, 2500-3000, 3000 m 500 Pradhan and Kim, 2017<75, 75-100, 100-150, 150-200, 200-250, >250 yok Pradhan et al, 20140-20, 20-25, 25-35, 35-65, 65-115, 115-252, 252-465, 465-705, 705-950, 950-2,000 yok Raja et al, 2017<50, 50-100, 100-150, 150-200, >200 50 Rozos et al, 2010<250, 250-500, 501-800, 801-1200, >1200 yok Saadatkhah et al, 20140-50, 50-100, 100-200, 200-300, >300 yok Sadr et al, 20140-100, 100-200, 200-300, 300-425 100 Shrestha et al, 2017<1281, 1281-1755, 1755-2254, 2254 yok Simon et al, 2017<30, 30-60, 60-90, >90 30 Tazik et al, 2014500-1000, 1000-1500, 1500-2000, >2000 500 Tsangaratos and Ilia, 2016<400, 401-600, 601-800, >801 200 Umar et al, 0-9.01, 9.01-18.02, 18.02-45.07, 45.07-90.14, 90.14-135,21, 135.21-198.31, 198.31-297.45, 297.46-458.73, 468.73-748.18, 748.18-2298 yok Wang et al, 20-850, 850-1000, 1000-1150, 1150-1300, 1300-1560yok...…”

Pemerhatian Arus Ionosfera semasa Suar Suria Kuat Menggunakan Data Magnetometer Dasar

“…Atmosfera bumi terdiri daripada beberapa lapisan bermula daripada lapisan troposfera sehingga ke termosfera. Terdapat pelbagai kajian telah dijalankan untuk menyelidik lapisan atmosfera ini (Shojanoori et al 2016;Simon et al 2017). Namun begitu, penyelidikan ini memfokuskan pada lapisan ionosfera.…”