Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe

  • Elia, M., Lovreglio, R., Ranieri, N., Sanesi, G. & Lafortezza, R. Cost-effectiveness of fuel removals in mediterranean wildland-urban interfaces threatened by wildfires. Forests 7, 149 (2016).

    Article 

    Google Scholar
     

  • Hamilton, M., Fischer, A. P., Guikema, S. D. & Keppel-Aleks, G. Behavioral adaptation to climate change in wildfire-prone forests. WIREs Clim. Change 9, e553 (2018).

    Article 

    Google Scholar
     

  • Paveglio, T. B., Stasiewicz, A. M. & Edgeley, C. M. Understanding support for regulatory approaches to wildfire management and performance of property mitigations on private lands. Land Use Policy 100, 104893 (2021).

    Article 

    Google Scholar
     

  • Ghorbanzadeh, O. et al. Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire 2, 43 (2019).

    Article 

    Google Scholar
     

  • Jain, P. et al. A review of machine learning applications in wildfire science and management. arXiv:2003.00646 [cs, stat] (2020).

  • Elia, M. et al. Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks. Environ. Impact Assess. Rev. 85, 106474 (2020).

    Article 

    Google Scholar
     

  • Oliveira, S., Rocha, J. & Sá, A. Wildfire risk modeling. Curr. Opin. Environ. Sci. Health 23, 100274 (2021).

    Article 

    Google Scholar
     

  • Langer, M. et al. What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research. Artif. Intell. 296, 103473 (2021).

    MathSciNet 
    Article 

    Google Scholar
     

  • Gunning, D. et al. XAI—Explainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).

  • Gunning, D. & Aha, D. DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 40, 44–58 (2019).


    Google Scholar
     

  • Guidotti, R. et al. A survey of methods for explaining black box models. ACM Comput. Surv. 51, 93:1–93:42 (2018).

  • Lipton, Z. C. In machine learning, the concept of interpretability is both important and slippery. Mach. Learn. 28.

  • Lombardi, A. et al. Explainable deep learning for personalized age prediction with brain morphology. Front. Neurosci. 15, (2021).

  • Amoroso, N. et al. A roadmap towards breast cancer therapies supported by explainable artificial intelligence. Appl. Sci. 11, 4881 (2021).

    CAS 
    Article 

    Google Scholar
     

  • Ngoc Thach, N. et al. Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study. Ecol. Inf. 46, 74–85 (2018).

  • Angelis, A. D., Ricotta, C., Conedera, M. & Pezzatti, G. B. Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions. PLoS ONE 10, e0116875 (2015).

    Article 

    Google Scholar
     

  • Guo, F. et al. Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood. Appl. Geogr. 66, 12–21 (2016).

    Article 

    Google Scholar
     

  • Elia, M., Giannico, V., Lafortezza, R. & Sanesi, G. Modeling fire ignition patterns in Mediterranean urban interfaces. Stoch Environ. Res. Risk Assess 33, 169–181 (2019).

    Article 

    Google Scholar
     

  • le Maire, G. et al. MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. Remote Sens. Environ. 115, 2613–2625 (2011).

    Article 

    Google Scholar
     

  • Ricotta, C. & Di Vito, S. Modeling the Landscape drivers of fire recurrence in Sardinia (Italy). Environ. Manage. 53, 1077–1084 (2014).

    Article 

    Google Scholar
     

  • Rodrigues, M. & de la Riva, J. An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environ. Model. Softw. 57, 192–201 (2014).

    Article 

    Google Scholar
     

  • Valese, E., Conedera, M., Held, A. C. & Ascoli, D. Fire, humans and landscape in the European Alpine region during the Holocene. Anthropocene 6, 63–74 (2014).

    Article 

    Google Scholar
     

  • Vilar del Hoyo, L., Martín Isabel, M. P. & Martínez Vega, F. J. Logistic regression models for human-caused wildfire risk estimation: Analysing the effect of the spatial accuracy in fire occurrence data. Eur. J. Forest Res. 130, 983–996 (2011).

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 

    Google Scholar
     

  • Barredo Arrieta, A. et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).

  • Getis, A. & Ord, J. K. The Analysis of Spatial Association by Use of Distance Statistics. in Perspectives on Spatial Data Analysis (eds. Anselin, L. & Rey, S. J.) 127–145 (Springer, 2010). https://doi.org/10.1007/978-3-642-01976-0_10.

  • Bivand, R. R packages for analyzing spatial data: A comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).

    Article 

    Google Scholar
     

  • Cilli, R. et al. Machine learning for cloud detection of globally distributed sentinel-2 images. Remote Sens. 12, 2355 (2020).

    Article 

    Google Scholar
     

  • Kaufman, S., Rosset, S., Perlich, C. & Stitelman, O. Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data 6, 15:1–15:21 (2012).

  • LeDell, E. & Poirier, S. H2O AutoML: scalable automatic machine learning. 16.

  • Altmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: A corrected feature importance measure. Bioinformatics 26, 1340–1347 (2010).

    CAS 
    Article 

    Google Scholar
     

  • Celik, E. Vita: Variable importance testing approaches. R package version (2015).

  • Sundararajan, M. & Najmi, A. The Many Shapley Values for Model Explanation. in Proceedings of the 37th International Conference on Machine Learning 9269–9278 (PMLR, 2020).

  • Merrick, L. & Taly, A. The explanation game: Explaining machine learning models using shapley values. in Machine Learning and Knowledge Extraction (eds. Holzinger, A., Kieseberg, P., Tjoa, A. M. & Weippl, E.) 17–38 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-57321-8_2.

  • Ertugrul, M., Varol, T., Ozel, H. B., Cetin, M. & Sevik, H. Influence of climatic factor of changes in forest fire danger and fire season length in Turkey. Environ. Monit. Assess 193, 28 (2021).

    CAS 
    Article 

    Google Scholar
     

  • Moreira, F. et al. Wildfire management in Mediterranean-type regions: Paradigm change needed. Environ. Res. Lett. 15, 011001 (2020).

    Article 

    Google Scholar
     

  • Romps, D. M., Seeley, J. T., Vollaro, D. & Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 346, 851–854 (2014).

    CAS 
    Article 

    Google Scholar
     

  • Elia, M. et al. Uncovering current pyroregions in Italy using wildfire metrics. Ecol. Process. 11, 15 (2022).

    Article 

    Google Scholar
     

  • Elia, M., Giannico, V., Spano, G., Lafortezza, R. & Sanesi, G. Likelihood and frequency of recurrent fire ignitions in highly urbanised Mediterranean landscapes. Int. J. Wildland Fire https://doi.org/10.1071/WF19070 (2020).

    Article 

    Google Scholar
     

  • Rodrigues, M., Costafreda-Aumedes, S., Comas, C. & Vega-García, C. Spatial stratification of wildfire drivers towards enhanced definition of large-fire regime zoning and fire seasons. Sci. Total Environ. 689, 634–644 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Salis, M. et al. Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy. Int. J. Disaster Risk Reduct. 102189 (2021). https://doi.org/10.1016/j.ijdrr.2021.102189.

  • Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A. & Pereira, J. M. C. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. For. Ecol. Manage. 275, 117–129 (2012).

    Article 

    Google Scholar
     

  • Curt, T. et al. Modelling the spatial patterns of ignition causes and fire regime features in southern France: Implications for fire prevention policy. Int. J. Wildland Fire 25, 785–796 (2016).

    Article 

    Google Scholar
     

  • Ascoli, D., Moris, J. V., Marchetti, M. & Sallustio, L. Land use change towards forests and wooded land correlates with large and frequent wildfires in Italy. Ann. Silvicult. Res. 46, (2021).

  • Mancini, L. D., Corona, P. & Salvati, L. Ranking the importance of Wildfires’ human drivers through a multi-model regression approach. Environ. Impact Assess. Rev. 72, 177–186 (2018).

    Article 

    Google Scholar
     

  • Narayanaraj, G. & Wimberly, M. C. Influences of forest roads on the spatial patterns of human- and lightning-caused wildfire ignitions. Appl. Geogr. 32, 878–888 (2012).

    Article 

    Google Scholar
     

  • Ganteaume, A. et al. A review of the main driving factors of forest fire ignition over Europe. Environ. Manage. 51, 651–662 (2013).

    Article 

    Google Scholar
     

  • Romero-Calcerrada, R., Novillo, C. J., Millington, J. D. A. & Gomez-Jimenez, I. GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landscape Ecol 23, 341–354 (2008).

    Article 

    Google Scholar
     

  • Bebi, P. et al. Changes of forest cover and disturbance regimes in the mountain forests of the Alps. For. Ecol. Manage. 388, 43–56 (2017).

    CAS 
    Article 

    Google Scholar
     

  • Vacchiano, G., Foderi, C., Berretti, R., Marchi, E. & Motta, R. Modeling anthropogenic and natural fire ignitions in an inner-alpine valley. Nat. Hazard. 18, 935–948 (2018).

    Article 

    Google Scholar
     

  • Conedera, M. et al. Characterizing Alpine pyrogeography from fire statistics. Appl. Geogr. 98, 87–99 (2018).

    Article 

    Google Scholar
     

  • Bajocco, S., Ferrara, C., Guglietta, D. & Ricotta, C. Fifteen years of changes in fire ignition frequency in Sardinia (Italy): A rich-get-richer process. Ecol. Ind. 104, 543–548 (2019).

    Article 

    Google Scholar
     

  • Arndt, N., Vacik, H., Koch, V., Arpaci, A. & Gossow, H. Modeling human-caused forest fire ignition for assessing forest fire danger in Austria. iForest Biogeosci. For. 6, 315 (2013).

  • D’Este, M. et al. Modeling fire ignition probability and frequency using Hurdle models: A cross-regional study in Southern Europe. Ecol. Process. 9, 54 (2020).

    Article 

    Google Scholar
     

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