Abstract. Many slow to moderate landslides are monitored in order to react on time and prevent loss of lives and reduce material damage. In most of such cases there are very limited data on the geometry, hydrogeological and material properties of the landslide. The aim of the paper is to test the ability of artificial neural networks (ANN) to make reliable short term predictions of rainfall induced landslide movements based on normally available data: rainfall and measured displacements. The back propagation artificial neural network was trained and tested for two sliding phenomena, which are very different in nature. One is moderately moving earthflow and the other very slow landslide, with maximum rate of movements 600 mm/day and 0.094 mm/day, respectively. The results show that in both cases a trained ANN can predict landslide movements with sufficient reliability and can therefore be used together with weather forecast to assist authorities when faced with difficult decisions, such as evacuation. The accuracy of the ANN prediction of movements depends on the type and architecture of ANN as well as on the organisation of the input data used for training, as it is shown by case histories.
This preprint has been withdrawn.
How to cite. Logar, J., Turk, G., Marsden, P., and Ambrožič, T.: Prediction of rainfall induced landslide movements by artificial neural networks, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2017-253, 2017.
Paper shows that artificial intelligence (AI) is able to make reliable short term predictions of rainfall induced landslide movements based on past measurements of daily rainfall and landslide movements. The procedure has been successfully tested on two different sliding phenomena with maximum rate of movements 600 and 0.094 mm/day, respectively. The goal of the research is to use AI to support hard decisions of civil protection (e.g. evacuation) when weather forecast predicts heavy rainfall.
Paper shows that artificial intelligence (AI) is able to make reliable short term predictions of...