Alluvioni: come migliorarne la previsione
E’ ancora in corso la COP27 e la speranza che riescano a trovare una convergenza su misure comuni per abbassare drasticamente il nostro impatto sul pianeta Terra è sempre accesa: non potrebbe essere altrimenti.
Tra gli effetti più evidenti dei cambiamenti climatici ci sono gli eventi meteo estremi che si presentano con maggiore frequenza ed intensità. Conseguenza di tutto ciò, soprattutto in un ambiente fortemente urbanizzato come il Veneto, sono gli eventi alluvionali.
In questo contesto, l’articolo che vi presentiamo, vuole proporre una nuova metodologia per prevedere con più accuratezza le dinamiche alluvionali nella città Metropolitana di Venezia.
Buona lettura.
Listing Details
Titolo:
Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice
Autore:
Zanetti Marco
Rivista:
Journal of Hydrology
Anno:
2022
Parole chiave:
Pluvial flood risk; Machine learning; Spatio-temporal cross-validation; Triggering factors; Forward feature selection; Metropolitan city of Venice
Citazione:
Zanetti Marco, Allegri Elena, Sperotto Anna, Torresan Silvia, Critto Andrea, Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice, Journal of Hydrology, Volume 612, Part B, 2022, 128150, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2022.128150. (https://www.sciencedirect.com/science/article/pii/S0022169422007247) Abstract: Due to a combination of climate change and urbanization, the instances of pluvial flooding are expected to increase in the next decades posing raising threats to properties, people and productive assets. Predicting and mapping pluvial flood-prone areas is becoming a crucial step in flood mitigation and early warnings, as well as climate change adaptation strategies, to be incorporate in urban planning. Most commonly applied machine learning (ML) procedures for pluvial flood risk assessment, neglect to account for spatio-temporal constraints, leading to overoptimistic models that underestimate the prediction error. In this paper, we propose a novel ML-based methodology for pluvial flood risk prediction in the Metropolitan City of Venice which, introducing a features selection process and spatio-temporal cross-validation, permits to reduce overfitting of the resulting ML models. Spatio-temporal characteristics of floods are derived from a dataset of 60 historical events occurred in the area between 1995 and 2020. Logistic Regression (LR), Neural Networks (NN) and Random Forest (RF) models are applied to identify and prioritize sub-areas that are more likely to be affected by pluvial flood risk, considering the daily precipitation amount and 12 different triggering factors. The models were validated using Random Cross-Validation (R-CV) and Leave Location and Time Out cross-validation (LLTO-CV), that split data in training and validation set considering both time and space. In addition, a forward features selection procedure was applied to identify the features, among the triggering factors, that better face spatio-temporal overfitting in pluvial flood prediction based on the Area Under the Curve (AUC) score. Results suggest that Logistic Regression and LLTO-CV represent the most reliable model to predict pluvial flood events in new spatio-temporal conditions, while, among the triggering factors, distance to river and distance to road resulted the prominent ones. Keywords: Pluvial flood risk; Machine learning; Spatio-temporal cross-validation; Triggering factors; Forward feature selection; Metropolitan city of Venice