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AN INVERSE ANALYSIS OF UNOBSERVED TRIGGER FACTOR ACCORDING TO SLOPE FAILURE TYPES

This paper discusses an inverse analysis of unobserved trigger factors according to “slope failure types” based on Structural Equation Modeling (SEM). Quantitative prediction models generally elucidate the relationship between past slope failures and causal factors (e.g., geology, topography, soil, slope, aspect, drainage, land cover, etc.), but do not consider trigger factors (e.g., rail fall, earthquake, weathering, etc.), due to difficulties in pixel-by-pixel observations of trigger factors. As one of the measures, an inverse analysis algorithm on trigger factor has been proposed according to the following steps: Step 1: The relationship between past slope failures (i.e., the endogenous variables), causal and trigger factors (i.e. the exogenous variables) are delineated on the path diagram used in SEM approach, Step 2: The regression weights in the path diagram are estimated based on the maximum likelihood estimation procedure; and Step 3: Through the “measurement equation” defined between the causal factors (i.e., observed variables) and the trigger factor (i.e., unobserved latent variable), the trigger factor is inversely estimated, and a trigger factor influence (TFI) map is also produced. As a subsequent subject from previous study, the TFI maps are produced with respect to “different types” of slope failures which are “surface failure,” “deep-seated slope failures,” and “landslides,” separately. As a final outcome, the differences in these TFI maps are delineated on a “difference (DIF) map.” These DIF maps enable us to analyze the difference of trigger factor influence with respect to slope failure types simultaneously. Such “heuristic information” would be useful for assessing the hazardous area with respect to the different types of slope failures, as well as for improving the cost-effectiveness in locating various slope measuring systems (e.g., the tensiometer, the rain gage, etc.).