Landslide prediction with model switching

Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environment data collected via wireless sensor networks (WSN). However, environment data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is thus critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANN weight coefficients can be made precise enough so that the network works similar to a human brain. However, when we have an imbalanced distribution of data, ANNs will not be able to learn the pattern of minority class, that is, the class of very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors according to environmental states. Our proposed method can improve prediction performance, and the landslide prediction system can give warnings in an average of 44 minutes prior to landslide occurrence.