![]() ![]() The model was implemented in Baota District, Shaanxi province, China. It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). Finally, we illustrate possible applications of landslide early warning systems for the operational forecasting of rainfall-induced landslides. Moreover, we depict a quantitative method for the validation of the thresholds. We describe in detail the frequentist method, which allows defining objective and reproducible thresholds at different non-exceedance probabilities, and the associated uncertainties. Next, we describe a widely used empirical method for the prediction of landslide initiation, i.e., rainfall thresholds. After describing the general characteristics of the two approaches, we discuss a grid-based slope stability model for the spatial and temporal prediction of rainfall-induced landslides. Hence, the prediction of the occurrence of rainfall-induced landslides is a key issue.Įvaluation of the relationships between rainfall and landslides can be made using physically-based or empirical approaches. Such phenomena pose serious threats to population and infrastructures. Rainfall causes changes in surface and groundwater dynamics that reduce the slope stability conditions and cause landslides. Eventually, we draw some conclusions on the likely uses of our work by providing recommendations for environmental management on this very delicate issue. Database structure and data analysis are then illustrated. Collecting information from different types of sources, a catalogue of some 1190 sinkhole events is built. We illustrate the methodology used to build the database, with particular focus on accuracy and reliability of the data. After introducing sinkholes, which is definitely a highly underrated type of disaster in Italy, we point out their occurrence in the country. We present a catalogue on natural and anthropogenic sinkholes in Italy, as the first step toward evaluation of the sinkhole hazard. They cause serious damage to infrastructures, economic activities, and human health every year. In Italy, sinkholes interest large sectors of the country, given the very long history of Italy with an intense utilization of the underground. Sinkholes can also be originated in relation to artificial cavities, excavated by man in past times. Nevertheless, other types of sinkholes can be formed through solution, suffusion and sagging processes. Sinkholes are a widespread geological hazard, typical of karst lands, where they generally originate as collapse features related to presence of underground voids. ![]()
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