Abstract
Landslides have become a common lithospheric hazard in the mountainous areas of Bangladesh, increasing rapidly in the last 20 years. This study aims to produce a landslide susceptibility map of Rangamati District using and comparing frequency ratio (FR), logistic regression (LR) models, and the combination of the two models. Among the 261 landslide locations, 75% were used as training data and 25% were used as testing data by random selection. Training data was used for landslide susceptibility mapping and the testing data was used for validating the map. Fourteen causative factors of elevation, aspect, geology, distance to road, distance to faults, distance to stream, stream density, plan curvature, profile curvature, normalized difference vegetation index, bare soil index, rainfall, and slope degree have been used to prepare the susceptibility map. The importance of the causative factors varies according to the model. ROC was used to validate the map. ROC values of 0.836, 0.816, and 0.842 were found for training data and 0.808, 0.758, and 0.826 were found for testing data for FR, LR, and FR integrated LR, respectively. Integration of the models slightly improved the accuracy. The outcome of the research will be helpful for landslide hazard assessment and policymaking. The study is indispensable to reveal the landslide-susceptible zone in the region and landslide can be mitigated by proper planning through this map.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Bibi Hafsa develops the idea of the research and read and corrected the manuscript for finalization. Md. Sharafat Chowdhury prepared the inventory database, analyzes the data, interprets them, and prepared the manuscript. Md. Naimur Rahman prepared the inventory database and makes them useable in GIS format.
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Hafsa, B., Chowdhury, M.S. & Rahman, M.N. Landslide susceptibility mapping of Rangamati District of Bangladesh using statistical and machine intelligence model. Arab J Geosci 15, 1367 (2022). https://doi.org/10.1007/s12517-022-10607-3
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DOI: https://doi.org/10.1007/s12517-022-10607-3