Abstract: Over the last decades, the development of Geographic Information System (GIS) technology has provided a method for the evaluation of landslide hazard. Through the use of directreverse DEM technology, the Changshougou valley is divided into 216 slope units, which includes 123 landslide units. According to mechanism analyses of landslides in the study area, six environmental factors are selected to evaluate the landslide occurrence, such as elevation, slope, aspect, curvature, distance to rivers, and human activities. Each factor is extracted in terms of slope unit within the scope of ArcGIS. The spatial analysis shows that most of landslides in the Changshougou valley are located at the elevation ranging from 100 to 150 m, with an aspect of 135°~225° and 40°~60° in slope, and on convex slopes, which are also influenced by hydrological processing and human activities. After the spatial analysis of environmental factors, this paper presents a case study for landslide hazard prediction, using back-propagation artificial neural network modeling optimized by genetic algorithms. Parameters of genetic algorithms and neural networks are set. The population size is 100, crossover probability 065, mutation probability 001, momentum factor 060, learning rate 07, max learning number 10000, and target error 0000001 From a database of 216 landslides, 120 landslides are used for training neural network models, and 96 landslides are used for the validation of landslide susceptibility. Comparing landslide presence with a susceptibility map, it is noted that the prediction accuracy of landslide occurrence is 9302%, while the units without landslide occurrence is predicted with accuracy of 8113%. The verification shows satisfactory agreement with accuracy of 8646% between the susceptibility map and the landslide locations. In this case study, it is also found that some disadvantages can be overcome in the application of BP neural networks, for example, the low convergence rate and local minimum, after the optimization is carried out using genetic algorithm. In conclusion, we note that genetic algorithmBP neural networks are an effective method to predict landslide hazard with high accuracy.