Prediction of landslide tsunami run-up on a plane beach through feature selected MLP-based model

Abstract

We proposed new prediction models based on multilayer perceptron (MLP) which successfully predict the maximum run-up of landslide-generated tsunami waves and assess the role of parameters affecting it. The input is approximately 55,000 rows of data generated through an analytical solution employing slide’s cross section, initial submergence, vertical thickness, horizontal length, beach slope angle and the maximum run-up itself, along with its occurrence time. The parameters are first ranked through a feature selection algorithm and six models are constructed for a 9,000-row randomly sampled dataset. These MLP-based models led predictions with a minimum Mean Absolute Percentage Error of 1.1% and revealed that vertical slide thickness has the largest impact on the maximum tsunami run-up, whereas beach slope angle has minimal effect. Comparison with existing literature showed the reliability and applicability of the offered models. The methodology introduced here can be suggested as fast and flexible method for prediction of landslide-induced tsunami run-up.