Machine learning models for river flow forecasting in small catchments.

In consideration of ongoing climate changes, it has been necessary to provide new tools capable of mitigating hydrogeological risks. These effects will be more marked in small catchments, where the geological and environmental contexts do not require long warning times to implement risk mitigation measures. In this context, deep learning models can be an effective tool for local authorities to have solid forecasts of outflows and to make correct choices during the alarm phase.

Hydrodynamics and water quality of a highly anthropized wetland: the case study of the Massaciuccoli basin (Tuscany, Italy).

Owing to increasing anthropogenic impacts, wetlands have suffered a serious environmental decline in recent decades. The sustainable management of these natural resources is fundamental to maintain both the ecosystems and the economic activities. The Lake Massaciuccoli and nearby areas represent one of the largest residual coastal marshy areas in Tuscany (Italy).

High-resolution spatial analysis of temperature influence on the rainfall regime and extreme precipitation events in north-central Italy.

In the last few years, several works have analyzed rainfall regime changes with the increase of temperature as a result of global warming. These changes, documented mainly in northern Europe, still need to be clarified in the Mediterranean area. Many studies have identified sometimes contradictory trends according to the type of data used, the methodology, and the daily or subdaily types of events.

Deep learning models to predict flood events in fast-flowing watersheds.

This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood events lasting only a few hours, with the aid of hydrometric control stations. This methodology allows for the creation of a system able to identify flood events with acceptable errors within several hours' notice.

Beyond one-way determinism: San Frediano's miracle and climate change in Central and Northern Italy in late antiquity.

Unlabelled: Integrating palaeoclimatological proxies and historical records, which is necessary to achieve a more complete understanding of climate impacts on past societies, is a challenging task, often leading to unsatisfactory and even contradictory conclusions. This has until recently been the case for Italy, the heart of the Roman Empire, during the transition between Antiquity and the Middle Ages. In this paper, we present new high-resolution speleothem data from the Apuan Alps (Central Italy).

Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images.

Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.