Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.

This work developed models to identify optimal spatial distribution of emergency evacuation centers (EECs) such as schools, colleges, hospitals, and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh. The use of location-allocation models (LAMs) for evacuation in regard to flood victims is essential to minimize disaster risk. In the first step, flood susceptibility maps were developed using machine learning models (MLMs), including: Levenberg–Marquardt back propagation (LM-BP) neural network and decision trees (DT) and multi-criteria decision making (MCDM) method. Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic (AUROC) curve. Mathematical approaches in a geographic information system (GIS) for four well-known LAM problems affecting emergency rescue time are proposed: maximal covering location problem (MCLP), the maximize attendance (MA), p-median problem (PMP), and the location set covering problem (LSCP). The results showed that existing EECs were not optimally distributed, and that some areas were not adequately served by EECs (i.e., not all demand points could be reached within a 60-min travel time). We concluded that the proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation.