Abstract
Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners.
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Highlights
• A novel intelligence hybrid block neural network (HBNN) structure was proposed.
• The optimized HBNN was applied to produce landslide susceptibility map.
• Challenge of training speed and overfitting problem in large informative pixels was solved.
• Compared with other models, HBNN produced more consistent and higher degree of success.
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Abbaszadeh Shahri, A., Maghsoudi Moud, F. Landslide susceptibility mapping using hybridized block modular intelligence model. Bull Eng Geol Environ 80, 267–284 (2021). https://doi.org/10.1007/s10064-020-01922-8
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DOI: https://doi.org/10.1007/s10064-020-01922-8