Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Abstract

Abstract. The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map.

Structural health monitoring of engineered structures using a space-borne synthetic aperture radar multi-temporal approach: from cultural heritage sites to war zones

“…It was build starting in 1501 by the Count Giovan Carlo Tramontano and it was never finished. As observed in several other medieval sites of this region [32,33], the castle is characterized by evident collapses due to landslides, cracks, structural discontinuities, unstable rock blocks, accumulate debris and discontinuity crests signs of ongoing morphogenetic dynamics [34]. Analysis of the Italian Landslide Inventory (IFFI) revealed the presence of a dormant, relatively shallow landslide affecting the area of the castle and north-eastern slope of the Lapillo hill [40].…”

Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China

“…The second one is the commonly-used receiver operating characteristic (ROC) and the area under the ROC curve (AUC). Since a test with perfect discrimination always produces a curve passing through the upper left corner of the plot, the closer the ROC curve is to the upper left corner, the more accurate are the landslide predictive results [83,84]. The AUC value ranges from 0.5-1, and it is close to 1, representing that the model is perfectly reasonable for prediction [85].…”

Natural risk assessment and mitigation of cultural heritage sites in North‐eastern Romania (Valea Oii river basin)

“…At an international level, the studies dealing with the assessment of natural hazards and their effects on cultural heritage cover all geographical areas: in Europe – Italy (Canuti et al., ; Sdao et al., ; Sdao & Simeone, ), Cyprus (Hadjimitsis et al., ); Africa – Mali (Bedaux et al., ); the Americas – USA (Pederson et al., ), Peru (Klimeš, ); Asia – Georgia (Tarragüel et al., ). From this perspective, Romanian scientific literature has only a few relevant studies (Micle, ; Nicu, ; Nicu & Romanescu, ; Romanescu & Nicu, ).…”