A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China
This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution.
Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China
This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution.
Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China
Yigen Qin (author) / Genlan Yang (author) / Kunpeng Lu (author) / Qianzheng Sun (author) / Jin Xie (author) / Yunwu Wu (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China
| DOAJ | 2020
Landslide susceptibility evaluation based on FR-DNN coupling model: A case study on Yanyuan County
| DOAJ | 2024