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Hue, T.T., Duong, T.V., Toan, D.V., Nghinh, L.T., Minh, V.C., Pho, N.V., Xuan, P.T., Hoan, L.T., Huyen, N.X., Pha, P.D., Chinh, V.V. and Thom, B.V. (2004) Investigation and Assessment of the Types of Geological Hazard in the Territory of Vietnam and Recommendation of Remedial Measures. Phase II: A Study of the Northern Mountainous Province of Vietnam. Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, 361 p. (In Vietnamese)

has been cited by the following article:

• TITLE: Rainfall Threshold Analysis and Bayesian Probability Method for Landslide Initiation Based on Landslides and Rainfall Events in the Past

  AUTHORS: Hien Minh Do, Kun Long Yin

  KEYWORDS: Rainfall Threshold, Antecedent Rainfall, Bayesian Probability, Ha Giang

  JOURNAL NAME: Open Journal of Geology, Vol.8 No.7, July 23, 2018

  ABSTRACT: In this paper a rainfall threshold and a Bayesian probability model are presented for the landslide occurrence of shallow landslides in Ha Giang city and the surroundings, Vietnam. The model requires the data on daily rainfall combined with the actual dates of landslide occurrences. Careful study on the database is a prerequisite for the paper. For this reason, selecting the input data was carried out carefully to ensure the reliable results of the study. The daily rainfall data covering a time span of 57 years was collected from a unique rain gauge station of National Centre for Hydro-meteorological Forecasting of Vietnam (from 1957 to 2013) and a landslide database with some landslides (37 of total of 245 landslides) that containing dates of occurrence, was prepared from historical records for the period 1989 to 2013. Rainfall thresholds were generated for the study area based on the relationship between daily and antecedent rainfall of the landslide events. The results shows that 3-day antecedent rainfall (with the rainfall threshold was established: RT = 40.8 − 0.201R3ad) gives the best fit for the existing landslides in the landslide database. The Bayesian probability model for one-dimensional case was established based on 26 landslides for the period 1989 to 2009, daily rainfall data with the same time and the values of probability varies from 0.03 to 0.44. Next, the Bayesian probability model for two-dimensional case was generated based on 11 landslides, rainfall intensity and duration in three months (May, June and July) of 2013 and the values of probability ranges from 0.08 to 0.67, and computed values of conditional landslide probability P(A|B) from two-dimensional case of Bayesian approach are clearly controlled by rainfall intensity > 40 mm with rainfall duration > 0.3 day.