| Popis: |
Landslide susceptibility assessment plays a critical role in disaster management and post-disaster planning. Machine learning-based approaches have recently attracted a lot of attention. However, the parameters tuning in this category of methods has not been accurately determined and is even considered as a weak point. The main objective of this study is to develop two machine learning-based landslide susceptibility models that optimized using a metaheuristic optimization algorithm, the grey wolf optimizer (GWO), for assessing the probability of landslide occurrence without artificial tuning. The selected machine learning algorithm were random forests (RF) and support vector machines (SVM). We apply the optimized models to Jiuzhaigou County on the eastern margin of Qinghai-Tibet Plateau. A total of 270 earthquake-triggered landslides were identified by remote sensing interpretation and filed surveys. Sixteen predisposing factors involving geology, human activity, and hydrology were extracted from the available materials. Then thirteen factors suitable for the study area were selected using multicollinearity diagnosis methods. Two meta-optimization models, GWO-RF, GWO-SVM, were con-structed after GWO's automated search for model parameters. Finally, the Receiver Operating Characteristic (ROC) curve and related statistics, including Accuracy, Sensitivity, and Specificity, were chosen to evaluate and compare the performance of the optimized landslide susceptibility models. Both models were constructed with ROCs higher than 0.95 on the training dataset and validation dataset as well as high accuracy. GWO-RF obtained the best both of accuracy and AUC values of 0.9198 and 0.972 on the validation dataset, respectively. Furthermore, we performed a weighting analysis of the factors and speculated on the relationship between the raw data distribution and accuracy. The results of this study show that the construction of the landslide susceptibility model optimized using a metaheuristic optimization algorithm is a feasible approach. |
