Abstract: This paper proposes a neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The classification capability was compared with the flood hazard mapping River Basin Plans (Piani Assetto Idrogeologico, acronimed as PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale), encoding four different increasing flood hazard levels. The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.
Abstract: The interaction of tunneling or mining with
groundwater has become a very relevant problem not only due to the
need to guarantee the safety of workers and to assure the efficiency of
the tunnel drainage systems, but also to safeguard water resources
from impoverishment and pollution risk. Therefore it is very
important to forecast the drainage processes (i.e., the evaluation of
drained discharge and drawdown caused by the excavation). The aim
of this study was to know better the system and to quantify the flow
drained from the Fontane mines, located in Val Germanasca (Turin,
Italy). This allowed to understand the hydrogeological local changes
in time. The work has therefore been structured as follows: the
reconstruction of the conceptual model with the geological,
hydrogeological and geological-structural study; the calculation of
the tunnel inflows (through the use of structural methods) and the
comparison with the measured flow rates; the water balance at the
basin scale. In this way it was possible to understand what are the
relationships between rainfall, groundwater level variations and the
effect of the presence of tunnels as a means of draining water.
Subsequently, it the effects produced by the excavation of the mining
tunnels was quantified, through numerical modeling. In particular,
the modeling made it possible to observe the drawdown variation as a
function of number, excavation depth and different mines linings.