Ahmad Qasim Akbar’s (Graduate School of Engineering) paper has been accepted for Water, MDPI.
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Authors
Ahmad Qasim Akbar, Yasuhiro Mitani, Ryunosuke Nakanishi, Hiroyuki Honda,Hisatoshi Taniguchi, Ibrahim Djamaluddin
Affiliation
Department of Civil Engineering, Graduate School of Engineering
Manuscript Title
Integrated Statistical Modeling for Regional Landslide Hazard Mapping in 0-Order Basins
Abstract
Rainfall-induced slope failures are among the most frequent and destructive natural hazards in Japan’s mountainous regions, often causing severe loss of life and damage to infrastructure. This study presents an integrated statistical framework for regional-scale landslide hazard mapping, with a focus on 0-order basins. To enhance spatial prediction accuracy, both bivariate and multivariate statistical models are employed. Bivariate models efficiently assess the relationship between individual conditioning factors and landslide occurrences but assume variable independence. Conversely, multivariate models account for multicollinearity and the combined effects of interacting factors, although they often require more complex data processing and may lack spatial clarity. To leverage the strengths of both approaches, two hybrid models were developed and applied to a 242.94 km² area in Fukuoka Prefecture, Japan. Model validation was performed using a matrix-based evaluation supported by a threshold optimization algorithm. Among the models tested, the hybrid Frequency Ratio–Logistic Regression (FR + LR) model demonstrated the highest predictive performance, achieving a success rate of 84.30%, a false alarm rate of 17.88%, and a miss rate of 12.30%. It effectively identified critical slip surfaces within zones classified as ‘High’ to ‘Very High’ susceptibility. This integrated approach offers a statistically robust, scalable, and interpretable solution for landslide hazard assessment in geomorphologically complex terrains. It provides valuable support for regional disaster risk reduction and contributes directly to achieving the Sustainable Development Goals (SDGs).
Journal name
Water, Volume 17, Issue 17
Relevant SDGs
SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action)
Comments
This study provides a robust and innovative framework for landslide hazard mapping in 0-order basins, offering improved predictive accuracy through integrated statistical models. Its outcomes support evidence-based disaster risk reduction, early warning, and land-use planning, thereby contributing to safer communities and enhanced resilience against rainfall-induced slope failures.
