The management of disposed waste in illegal landfills (ILs) is a significant problem in contemporary societies due to respective hazards for the environment and human health. This paper presents a characterisation of ILs on the islands of La Palma (LP) and Gran Canaria (GC) based on multivariable statistical analysis. Inspection of numerous sites on both islands revealed a total of 153 and 286 ILs on LP and GC, respectively. A geospatial database was created composed of different potentially explanatory features of different typology (177): waste type, control and vigilance, socioeconomic, accessibility, distance to elements of interest, visibility and physical. The degree of association between the explanatory features and the occurrence of ILs was analysed with the support of exploratory statistics and the multivariable analysis techniques of principal component analysis (PCA) and binary logistic regression (LR). PCA explained 82.34% and 81.83% of total data variance in LP and GC, respectively, considering 7 and 6 components (Kaiser–Mayer–Olkin; LP: 0.715; GC: 0.711). The LR models for LP and GC had an overall accuracy of 93.5% and 92.5%. In LP and GC, 6 of 23 features and 9 of 21 features were, respectively, selected. The features most associated with the occurrence of ILs were: in LP, building density, distance to agricultural spaces and distance to green zones; in GC, the industrial activity indicator, density of ground use transition to artificial covers, density of greenhouses and distance to communication routes.

The island of Gran Canaria (Spain) has undergone a significant transformation in wildfire dynamics over the past two decades, characterized by a decline in wildfire frequency but a marked increase in the severity and spatial impact of extreme events, particularly within the wildland–urban interface (WUI). This study analyzes wildfire activity between 2000 and 2020 using official datasets and statistical trend analyses, incorporating robust severity indicators and measures of burned area concentration. Results show a statistically significant decreasing trend in the number of wildfires, while burned area is extremely concentrated in a small number of high-intensity events, with four large wildfires accounting for more than 97% of the total affected area. Climatic influences on wildfire activity were assessed through the analysis of long-term meteorological indicators, focusing on trends in extreme heat days and precipitation as proxies for thermal stress and fuel moisture availability. The results indicate a substantial modification of the background climatic framework under which wildfires develop, although no direct causal relationships are inferred. In parallel, territorial processes—such as rural abandonment, increased fuel continuity, and the expansion of dispersed housing beyond consolidated settlements—act as key amplifiers of wildfire risk. Overall, the findings highlight a transition from emergency-oriented fire suppression toward resilience-based wildfire management, emphasizing the need to integrate climate adaptation, territorial planning, and stricter land-use regulation in WUI areas.

This paper presents a novel method, Area and Feature Guided Regularised Random Forest (AFGRRF), applied for modelling binary geographic phenomenon (occurrence versus absence). AFGRRF is a wrapper feature-selection method based on a previous modification of Random Forest (RF), namely the Guided Regularised Random Forest (GRRF). AFGRRF produces maps that minimise the affected area without a significant difference in accuracy. For this, it tunes the GRRF hyper-parameters according to a trade of between True Positive Rate and the affected area (Success Rate). AFGRRF also addresses the ‘Rashomon effect’ or the multiplicity of good models. The proposed method was tested to model illegal landfills in Gran Canaria Island (Spain). AFGRRF performance was compared to that of other RF-based methods: (i) standard RF; (ii) Area Random Forest (ARF); (iii) Feature Random Forest (FRF); (iv) Area Feature Random Forest (AFRF) and (v) GRRF. AFGRRF predicted the smallest affected area, 19% of the island, at a similar True Positive Rate. This percentage is substantially smaller than the one predicted by RF (27.43%), ARF (26%), FRF (27.78%), AFRF (23%) and GRRF (29.67%).