The influence of sampling on landslide susceptibility mapping using artificial neural networks

Abstract

Landslides can have serious environmental, economic and social consequences. Using artificial neural networks (ANN) to map these landslides is becoming more frequent every year, being one of the most reliable methods for this. Among the prime influences on the generated maps, sample areas are significantly interesting, since they directly influence the result. In this research, we investigated how the performance of these models is influenced by the use of partial sampling (with landslides caused by a single precipitation event–Single Model) or total (with landslides caused by multiple precipitation events–Full Model). This is one of the main topics that our study approaches. This study aims to evaluate the criteria for landslide sampling and ANN modeling by analyzing the influence of distance on the sampling processes, the use of multiple landslide events, and the relationship between terrain attributes and susceptibility models. To this end, were used five sampling areas (1638 points samples of landslides) in the Serra Geral, southern Brazil, distance buffers in the non-occurrence sampling process (2–40 km), and 16 terrain attributes. The training of the multilayer network was carried out by backpropagation algorithm, and the accuracy was calculated using the Area Under the Receiver Operating Characteristic Curve. The results showed that the greater the distances of the non-occurring samples, the greater the accuracy of the model, with a 40 km buffer resulting in the best models. They also showed that the use of multiple events (Full Model) produced better results than each event used separately (Single Model), obtaining accuracies of 0.954 and 0.931, respectively. This is mainly because there is greater differentiation between occurrence and non-occurrence samples when using multiple events, thus facilitating the distinction between more and less susceptible areas.