Cut slope hazard analysis and management based on a double-index precipitation threshold: a case study in the Miaoyuan area (Eastern China).

Zeng, Taorui; Jin, Bijing; Liu, Yang; Glade, Thomas; Wang, Fei; Yin, Kunlong; Peduto, Dario

The rapid development of rural regions, the mountainous landscape, and frequent subtropical-typhoon-related rainfall have collectively contributed to a high incidence of cut slope-induced landslides in the coastal areas of eastern China. Despite the escalating risk, there has been a noticeable absence of comprehensive hazard assessments and targeted management measures for private housing and road construction in these rural environments. This paper introduces a novel approach for mitigating such risks by employing a susceptibility evaluation framework grounded in machine learning and uncertainty methods, combined with a double-index rainfall intensity-duration (I-D) threshold model. The proposed Intelligent Slope Prevention System operates through a sequential four-step process: (i) Site-specific landslide susceptibility is assessed through cut slope feature investigations and the use of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN); (ii) the double-index model calculates rainfall thresholds, accounting for both prolonged continuous rainfall and short-term heavy rainfall events; (iii) the integration of rainfall thresholds with susceptibility assessments allows for the categorization of hazard levels; and (iv) tailored management strategies are deployed for data collection and early warning issuance. The study demonstrates that the SVM achieved the highest prediction accuracy across soil, rock-soil mixed, and rock slopes. The double-index model further enhanced the system's performance by predicting all 20 rainfall-induced landslides, with 15 of them falling under high or very high warning levels. An empirical evaluation during a heavy rainfall event on 29th June 2021 confirmed the system's effectiveness in identifying high-hazard areas and issuing timely warnings, thus significantly mitigating potential damage. Implemented in the coastal mountain basins of eastern China, the Intelligent Slope Prevention System leverages the gathered knowledge to manage and regulate slope hazards effectively, thereby enhancing the safety of both residential and infrastructural assets.

CHINA; ARTIFICIAL neural networks; MACHINE learning; RAINFALL; ROCK slopes; EMERGENCY management; LANDSLIDES; NATURAL disaster warning systems

Environmental Earth Sciences, 2024, Vol 83, Issue 24, p1

1866-6280

Academic Journal

10.1007/s12665-024-11987-3