Gilberto Pambianchi

Università di Camerino

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Bernardino Gentili

Università di Camerino

Marco Materazzi

Università di Camerino

Domenico Aringoli

Università di Camerino

Piero Farabollini

Università di Camerino

Matteo Gentilucci

Università di Camerino

F. Dramis

Roma Tre University

Marco Giacopetti

Università di Camerino

Maurizio Barbieri

Sapienza University of Rome

Mauro Coltorti

University of Siena

Younes Hamed

University of Gafsa

Cooperative Institutions

Università di Camerino

Sapienza University of Rome

Istituto Nazionale di Geofisica e Vulcanologia

University of Chieti-Pescara

Institute of Technology of Cambodia

Terra

Universidad de Granada

Consejo Superior de Investigaciones Científicas

Roma Tre University

University of Naples Federico II

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Valutazione della pericolosità geomorfologica lungo i sentieri escursionistici del Parco Nazionale dei Monti Sibillini (Appennino centrale)

Domenico Aringoli Piero Farabollini Gilberto Pambianchi

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Netectonic conditioning in the evolution of the monoclinal relief of the central-southern Marches (Central Italy).

Carlo Bisci Bernardino Gentili Marco Materazzi Gilberto Pambianchi

Central air conditioning

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Pliocene –Pleistocene geomorphological evolution of the Adriatic side of Central Italy

Geologica Carpathica (2017)

Bernardino Gentili Gilberto Pambianchi Domenico Aringoli Marco Materazzi Marco Giacopetti

Abstract This work is a significant contribution to knowledge of the Quaternary and pre-Quaternary morphogenesis of a wide sector of central Italy, from the Apennine chain to the Adriatic Sea. The goal is achieved through a careful analysis and interpretation of stratigraphic and tectonic data relating to marine and continental sediments and, mostly, through the study of relict limbs of ancient landscapes (erosional surfaces shaped by prevailing planation processes). The most important scientific datum is the definition of the time span in which the modelling of the oldest morphological element (the “summit relict surface”) occurred: it started during Messinian in the westernmost portion and after a significant phase during middle-late Pliocene, ended in the early Pleistocene. During the middle and late Pleistocene, the rapid tectonic uplift of the area and the climate fluctuations favoured the deepening of the hydrographic network and the genesis of three orders of fluvial terraces, thus completing the fundamental features of the landscape. The subsequent Holocene evolution reshaped the minor elements, but not the basic ones.

Early Pleistocene

10.1515/geoca-2017-0001

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Carta geomorfologica delle piane alluvionali dei fiumi Aso, Tesino e Tronto.

Carlo Bisci Mara Buccolini Leonardo Eusebio Bernardino Gentili Gilberto Pambianchi

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Geomophology and Quaternary evolution of central Italy

Domenico Aringoli Carlo Bisci Anna Maria Blumetti Marcello Buccolini Sirio Ciccacci

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Superficial effects induced by the 1997 earthquake in the Colfiorito area (Central Apennine, Italy)

Piero Farabollini Bernardino Gentili Marco Materazzi Gilberto Pambianchi Domenico Aringoli

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Relief, Intermontane Basins and Civilization in the Umbria-Marche Apennines: Origin and Life by Geological Consent

World geomorphological landscapes (2017)

Marta Della Seta Laura Melelli Gilberto Pambianchi

Landform

Sinkhole

River terraces

10.1007/978-3-319-26194-2_27

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A Multi-Model Approach Using Statistical Index and Information Criteria to Evaluate the Adequacy of the Model Geometry in a Fissured Carbonate Aquifer (Italy)

Water (2016)

Marco Giacopetti Ezio Crestaz Marco Materazzi Gilberto Pambianchi Kristijan Posavec

A conceptual model related to a mountain aquifer that is characterized by a lack of data of hydrogeological parameters and boundary conditions, which were based on a single available observational dataset used for calibration, was studied using numerical models. For the first time, a preliminary spatial-temporal analysis has been applied to the study area in order to evaluate the real extension of the aquifer studied. The analysis was based on four models that were characterized by an increasing degree of complexity using a minimum of two zones and a maximum of five zones, which consequently increased the number of adjustable parameters from a minimum of 10 to a maximum of 22, calibrated using the parameter estimation code PEST. Statistical index and information criteria were calculated for each model, which showed comparable results; the information criteria indicated that the model with the low number of adjustable parameters was the optimal model. A comparison of the simulated and observed spring hydrographs showed a good shape correspondence but a general overestimation of the discharge, which indicated a good fit with the rainfall time series and a probably incorrect extension of the aquifer structure: the recharge contributes more than half of the total outflow at the springs but is not able to completely feed the springs.

Geostatistics

10.3390/w8070271

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Segnalazione di alcuni episodi fluvio-lacustri nelle Marche centro-meridionali.

Geografia fisica e dinamica quaternaria (1988)

Bernardino Gentili Gilberto Pambianchi

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Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

Climate (2022)

Matteo Gentilucci Gilberto Pambianchi

The reconstruction of daily precipitation data is a much-debated topic of great practical use, especially when weather stations have missing data. Missing data are particularly numerous if rain gauges are poorly maintained by their owner institutions and if they are located in inaccessible areas.In this context, an attempt was made to assess the possibility of reconstructing daily rainfall data from other climatic variables other than the rainfall itself, namely atmospheric pressure, relative humidity and prevailing wind direction.The pilot area for the study was identified in Central Italy, especially on the Adriatic side, and 119 weather stations were considered.The parameters of atmospheric pressure, humidity and prevailing wind direction were reconstructed at all weather stations on a daily basis by means of various models, in order to obtain almost continuous values rain gauge by rain gauge. The results obtained using neural networks to reconstruct daily precipitation revealed a lack of correlation for the prevailing wind direction, while correlation is significant for humidity and atmospheric pressure, although they explain only 10–20% of the total precipitation variance. At the same time, it was verified by binary logistic regression that it is certainly easier to understand when it will or will not rain without determining the amount. In this case, in fact, the model achieves an accuracy of about 80 percent in identifying rainy and non-rainy days from the aforementioned climatic parameters. In addition, the modelling was also verified on all rain gauges at the same time and this showed reliability comparable to an arithmetic average of the individual models, thus showing that the neural network model fails to prepare a model that performs better from learning even in the case of many thousands of data (over 400,000). This shows that the relationships between precipitation, relative humidity and atmospheric pressure are predominantly local in nature without being able to give rise to broader generalisations.

10.3390/cli10080120

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Citations (4)