Abstract
Landslide susceptibility depends on various causal factors such as geology, land use/land cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time, LULC is a dynamic factor associated with human activities. This study evaluates the impact of LULC change on landslide susceptibility in the Rangamati municipality of Rangamati district, Bangladesh, based on three LULC scenarios—the existing (2018) LULC, the proposed LULC (proposed in 2010, but not yet implemented), and the simulated LULC of 2028—using artificial neural network (ANN)-based cellular automata. The random forest model was used for landslide susceptibility mapping. The model showed good accuracy for all three LULC scenarios (existing: 82.7%; proposed: 81.4%; and 2028: 78.3%) and strong positive correlations (>0.8) between different landslide susceptibility maps. LULC is either the third or fourth most important factor in these scenarios, suggesting that is has a moderate impact on landslide susceptibility. Future LULC changes will likely increase landslide susceptibility, with up to 14.5% increases in the high susceptibility zone for both the proposed and simulated LULC scenarios. These findings may help policymakers carry out proper urban planning and highlight the importance of considering landslide susceptibility in LULC planning.
Highlights
Landslides cause damage to infrastructure and casualties worldwide
We evaluated the change of landslide susceptibility using the proposed land use/land cover (LULC) plan and simulated the LULC of 2028 (BAU)
Our study revealed that elevation is the most important causal factor (Figure 4) in the study area since people want to carry out anthropogenic activities such as infrastructure development in areas where elevation is low
Summary
Landslides cause damage to infrastructure and casualties worldwide. As a representation of the spatial probability of landslides over an area [1,2], landslide susceptibility mapping is critical to mitigating landslide disasters [3,4,5]. Various statistical and machine learning models, including logistic regression, linear discriminant analysis, random forest, support vector machines, decision tree, extreme gradient boosting (XGBoost), frequency ratio, and certainty factor, have been used for landslide susceptibility mapping [7,9,10,11,12,13,14,15,16,17]. These models explore the relationships between landslide occurrences and causal factors to determine the spatial probability over the area [8,18,19]. Advanced machine learning models such as random forest and artificial neural networks (ANN) usually produce much higher accuracy, but less interpretability [21]
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