MACHINE LEARNING BASED MODEL FOR WARNING OF REGIONAL LANDSLIDE DISASTERS

The landslide disaster in China is serious, and early warning of regional landslide disaster is an important measure of disaster prevention and reduction. Then the study of the model is the key to carry out regional landslides warning successfully. This paper systematically proposes the construction method of the regional landslide disaster warning model based on machine learning, and takes Qingchuan County, Sichuan province as an example. Based on the geological and meteorological data of about ten years, the regional landslide disaster early warning model of Qingchuan County is built and an example verification is carried out. (1)The construction of regional landslide disaster warning model based on machine learning mainly includes several key steps including the construction of training sample set, sample training, parameter adjustment, model preservation and invocation. (2)A training sample set construction method for regional landslide early warning is proposed. That is, based on the positive samples, the negative samples are obtained by random sampling under spatial-temporal limitation, and the complete training sample set is finally obtained. (3)In the sample learning and training, 80% of the training sample set is taken as the training set, and 20% is taken as the test set, and 50-fold cross verification method is used. Then, Accuracy, ROC curve and AUC value verification model and model generalization ability are adopted. In order to achieve the optimal model, Bayesian Optimization Algorithm is used to optimize the model parameters. (4)In the early warning, the trained warning model is used to output the probability of landslide disaster. According to the probability, the early warning level is determined. When the output probability P≥40% and P < 60%, yellow alert can be issued; When the output probability P≥60% and P < 80%, an orange alert can be issued; and a red alert is issued when the output probability P≥80%. (5)Taking Qingchuan County as an example, six machine learning models were used for training. The results showed that the Random Forest model had the best performance, with the highest accuracy(0.963), no over-fitting phenomenon and the best generalization ability(AUC=0.986). The Second model is Logistic Regression model. Then, the model is artificial neural network model and decision tree model. We take the actual early-warning on June 26, 2018 as an example, load and call the pre-trained Random Forest model, calculate the output probability of the model, and divide the warning levels. According to the verification of the actual occurrence of landslide disasters, the result shows that all the landslides are within the warning area, among which 70.6% fall in the red warning area, 17.6% in the orange warning area and 11.8% in the yellow warning area.