INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION - A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN
Journal
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Journal Volume
43
Journal Issue
B3-2022
Date Issued
2022-05-30
Author(s)
YIN-KAI CHEN
Lin, Y. T.
Yen, H. Y.
Chang, N. H.
Lin, H. M.
Yang, K. H.
Chen, C. S.
Cheng, H. K.
Wu, H. H.
Han, J. Y.
Abstract
Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.
Subjects
InSAR | Landslide Prediction | Machine Learning | Slope Unit | Spatial-Temporal Factors | Spearman
Type
conference paper