Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide.

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning.

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions.

Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007–2015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO₂, O₃, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure–response relationships over lag periods of 0–1, 0–3, 0–5, and 0–10 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R² = 0.974; MAE = 0.019 cases/day; cross-validated R² = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO₂, and O₃ each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR > 1.20, CI > 1). Moderate associations were observed for NO₂ and PM10 (RR: 1.02–1.04). O₃ exhibited smaller but significant effects at shorter lags (RR = 1.01 at 0–1 days; RR = 1.02 at 0–3 days; CI > 1). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.

The COVID‐19 pandemic has generated significant global impacts on health and society, imposing a comprehensive analysis of its influencing factors, including weather variables. This study investigates the interaction between meteorological conditions and the spread of COVID‐19 in three Italian regions: Lombardia, Emilia‐Romagna, and Puglia. Effects of weather variables, such as air temperature, relative humidity, dew point, solar radiation, wind speed, and barometric pressure, are explored in the incidence of disease. Observed meteorological and health data are taken from various sources, such as the citizen‐science Meteonetwork Association and the National Department of Civil Protection, respectively, and they are analyzed with statistical methods and machine learning algorithms. The study emphasizes the necessity of carefully considering key meteorological quantities as primary drivers in illness diffusion and prevention strategies, offering valuable insights to address challenges to the pandemic and ensure the safety of global communities. The results reveal a significant correlation between specific atmospheric variables and the spread of COVID‐19, with dew point temperature as the most influential parameter at low air temperature values.