Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks

This study investigates the potential application of artificial neural networks in landslide susceptibility mapping in the Hoa Binh province of Vietnam. A landslide inventory map of the study area was prepared by combining landslide locations investigated through three projects during the last 10 years. Some recent landslide locations were identified based on SPOT satellite images, field surveys, and existing literature. The images have a spatial resolution of 2.5 m. Ten landslide conditioning factors were utilized in the multilayer feed-forward neural network analysis: slope, aspect, relief amplitude, lithology, land use, soil type, rainfall, distance to roads, distance to rivers and distance to faults. Two back-propagation training algorithms, Levenberg–Marquardt and Bayesian regularization, were utilized to determine synoptic weights using a training dataset. Relative importance of each landslide conditioning factor was assessed using the above mentioned synoptic weights. The final connection weights obtained in the training phase were applied to the entire study area to produce landslide susceptibility indexes. The results were then imported to a GIS and landslide susceptibility maps were constructed. Landslide locations not used in the training phase were used to verify and compare the results of the landslide susceptibility maps. Finally, the two landslide susceptibility maps were validated using the prediction-rate method. Subsequently, areas under the prediction curves were assessed. The prediction accuracy of landslide susceptibility maps produced by the Bayesian regularization neural network and the Levenberg–Marquardt neural network were 90.3% and 86.1% respectively. These results indicate that the two models seem to have good predictive capability. The Bayesian regularization network model appears more robust and efficient than the Levenberg–Marquardt network model for landslide susceptibility mapping.