Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran


Alimohammadlou Y., Najafi A., Gokceoglu C.

CATENA, cilt.120, ss.149-162, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

Özet

As is the case around the world, Azerbaijan province in northwestern Iran experiences numerous landslides that occur following intensive precipitation periods. These landslides damage many aspects of human life as well as the natural environment, and hence it should be evaluated accurately. However, one of the main challenges of landslide studies is the estimation of the periods between potential landslides, which would provide information that is useful for the development of warning systems and/or mitigation measures. The aim of the present study is to propose a novel approach utilizing artificial neural network and fuzzy clustering methods for landslide frequency estimation. This study also investigates the 2005 Saeen, Iran landslide triggered by prolonged heavy rainfall that affected groundwater levels, and introduces a methodology to estimate the date range of the next probable landslide. Based on the interpretation of the triggering factor and failure mechanism, the Saeen landslide was induced by the prolonged rainfall behavior and resultant deep infiltration of water between the years 2002 and 2005. During this period, the maximum rainfall values were observed in April of each year, and then followed by decreased rainfall to a minimum value in June and August. The results of this investigation revealed that the failure probability will likely increase in the next precipitation periods and the saturation rate will be high in August and September of 2017 and 2018, resulting in landslides. In conclusion, this method is only used for the heavy precipitation as the triggering factor to estimate and analyze the next potential landslide. The information derived from this method will establish a time window for future failure, where the other slope-stability factors can be evaluated and then utilized to set up more accurate and reliable networks for further investigations. (C) 2014 Elsevier B.V. All rights reserved.