A Data Based Model to Predict Landslide Induced by Rainfall in Rio de Janeiro City

Landslide prediction is complex and involves many factors, such as geotechnical, geological, topographical, and even meteorological. This work presents a methodology by using a Data Mining approach in order to predict landslide occurrences induced by rainfall in Rio de Janeiro city. Landslide and rain data records from 1998 to 2001 were obtained from field technical reports and 30 automatic rain gauges, respectively. It was also collected data regarding soil parameters, including urban areas, forest, vulnerability, among others, and totalizing 46 soil variables. All the information was inserted into a Geographic Information Systems. Clustering (Dendrogram and k-means) and Statistical (Principal Component Analysis and Correlation) techniques were used to regionalize the rain data and select the rain gauges to be input on Artificial Neural Networks , which were used to replace the missing rain values. The landslide volume variable also presented missing values and it was completed by the k-Nearest Neighbor method. After data preparation, some models were built to predict landslide and rainfall using Data Mining techniques. The obtained model’s performance is also analyzed.