Landslides Prediction and Detection Using IoT System

Abstract

The movement of rock, soil, or other material down a sloping area of terrain is referred to as a “landslide.” Rain, earthquakes, volcanoes, and other natural and manmade phenomena that render a slope unstable may all be the triggers for landslides. The Internet of Things (IoT) plays an important role in resolving the landslide problem. It poses a serious threat to humans and harms the world's various forms of poverty and surface environment. As we all know, landslides occur naturally, so we don't know when they occur. It is critical to obtain a precise location in order to rescue the people. We can get information from satellites and the different sensors that are embedded in them. It makes it simple to create landslide databases, locate landslide-prone areas, and create a safe environment for people and nature. We can avoid several environmental damages if we can detect or predict landslides. As a result, we must devise a solution that allows us to obtain information quickly and accurately. Landslides mainly occur due to climate change in the environment. The primary goal of this proposed work is to solve and avoid naturally occurring catastrophes or calamities by providing information as early as possible by computing the machine learning algorithm in the supplied data set. There are numerous reasons for establishing such an environment for disaster reduction, in which the sensor detects the element and immediately sends the data, and different learning algorithms are utilized to provide information to the people about the catastrophe. It is low-cost and simple to install, and it may be utilized by a semi-skilled individual. The algorithm is fed with various parameters and processed to gain its respective output. According to the output delivered by the algorithm, we are able to predict the condition of the particular site and further actions may be performed.