The landslide is a kind of natural disaster caused by slope instability. Due to its wide distribution, frequent occurrence, and damaging effects, many countries in the world suffer from it. Sichuan Province in China is a landslide-prone area since it locates in the seismic zone. In this paper, the Morgenstern-Price method is used to analyze the stability of a landslide in Sichuan Province, and heavy rainfall is also simulated to analyze the landslide stability under such conditions, which can provide assistance for geological disaster prevention in the future.

The rapid development of cities and the increasing complexity of its internal structure have led to pressing fire security problems, which calls for an effective and accurate comprehensive fire warning model. Most existing fire warning models predict only for a single fire scenario and can hardly balance the speed and accuracy in their predictions, which are not suitable for large-scale scenarios with complex structures. This paper proposes a fire warning model that includes both forest and building area based on Cellular Automata. Experimental areas were established to simulate fire warning according to the proposed hybrid model. The experimental results have shown that the model can quickly and accurately simulate the fire spread process and provide effective support for emergency decision-making in complex scenarios.