The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy)

This paper presents the results obtained by the elaboration of an artificial neuronal network for the creation of a rockfall susceptibility map. The analysis was carried out by analysing the predisposing and triggering factors of the rockfall phenomenon. The parameters considered for this study and representing the input data of the artificial neural network are factors such as: gradient, soil use, lithology, rockfall source areas and kinetic energy values obtained by considering the probable pathways of the blocks through simulations with dedicated softwares, DEMs and niches of the rockfalls that have already occurred in the past. The processing of this data (required in a versatile dedicated software for the realization of the artificial neural network in ASCII format) is done using GIS softwares, useful tools for the creation of hazard maps. An important step is the realization of the rockfall inventory map: it allows to identify the training set (consisting of 50% of the pixels relative to the rockfall niches) for the network training and the testing set (considering the remaining 50% of the pixels relative to the rockfall niches) to assess the network accuracy by overlaying the rockfall niches belonging to the testing set with the obtained susceptibility map

Hydrogeology and Hydrogeochemistry of the Lauria Mountains Northern Sector Groundwater Resources (Basilicata, Italy)

In this study, the hydrogeological characterization of the northern sector of the Lauria Mounts carbonate hydrostructure (southern Apennines, Basilicata region) has been carried out and the hydrochemical properties of different collected groundwater samples have been characterized. Several normal springs drain the hydrostructure, some of them characterized by high annual mean discharges. Groundwater samples were collected from different springs; many parameters such as pH, electrical conductivity, and total dissolved solids have been measured, and major (cations and anions) elements and stable isotopes have been analysed following standard test procedures. Other chemical characteristics were derived from the analysed quality parameters. The results elucidate that the main hydrogeochemical processes control the chemical content and assess the quality of the groundwater within the hydrostructure. The analyses highlight that the chemical compositions of groundwater are strongly influenced by the lithology, especially limestones and dolomitic limestones; they explain and confirm the hydrogeological setting of the system. The groundwater system displays light different geochemical signatures. The processes contributing to the concentrations of major ions depend primarily on carbonate dissolution. The analysis, in all studied groundwater samples, shows that the facies groundwater type is Ca–HCO3, bicarbonate is the dominant anion, and calcium is the dominant cation with appreciable magnesium concentrations. To identify the aquifer's recharge areas, the environmental stable isotopes oxygen and hydrogen, deuterium, and 18O were analysed. The unaltered δ18O and δD signatures for the groundwater of the major springs allows identifying the recharge area of these emergencies at elevations ranging from 900 m to 1000 m (a.s.l.), pointing out the presence of deeper flow regime feeding of these springs. The groundwater sample isotopic characteristics of D and 18O suggest that most of the groundwater is recharged directly by infiltration in a high-permeability medium

Seawater intrusion vulnerability assessment by Galdit method in the Metaponto coastal aquifer (Basilicata, Italy)

The groundwater vulnerability assessment to seawater intrusion (SWI), applying the GIS-based overlay-index GALDIT method, is provided for the Metaponto coastal aquifer (Basilicata region, southern Italy). The method is based on six conditioning parameters: groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater level (L), distance from the shore (D), impact of the existing status of SWI (I), and aquifer thickness (T). Three vulnerability classes were detected: low, moderate, and high, covering 70.40%, 22.65%, and 6.95% of the study area, respectively. The highest class is located close to the coastal sector due to the proximity to the sea, the greater thickness of the aquifer, and the shallow freshwater-seawater interface. To evaluate the sensitivity of the method on the predictive analysis and the influence of the single parameter and weight on the final vulnerability, the sensitivity analysis was carried out. The single-parameter analysis indicated that the factors such as groundwater table above sea level (a.s.l.), aquifer type, and impact of SWI have the greatest influence on the vulnerability. The application leads to the vulnerability mapping to SWI in the coastal plain that results to be a promising tool for decisionmaking finalized to properly manage groundwater

Eventi di pioggia e fasi di attività di una frana nei pressi di Calciano in Basilicata

Nel lavoro vengono illustrati ed interpretati i risultati degli studi geologici, geomorfologici ed idrologici condotti al fine di accertare i caratteri geomorfologici ed evolutivi di un complesso e profondo movimento di massa e, in particolare, ii ruolo svolto dalle piogge sulle sue periodiche rimobilitazioni prodottesi nd recente passato e che si sono rese responsabili di significativi daimi alla linea ferroviaria Potenza - Metapont