Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe | Scientific Reports -…

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. & S, 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. XAIExplainable artificial intelligence. Sci. Robot. 4, eaay7120 (2019).

Gunning, D. & Aha, D. DARPAs explainable artificial intelligence (XAI) program. AI Mag. 40, 4458 (2019).

Google Scholar

Guidotti, R. et al. A survey of methods for explaining black box models. ACM Comput. Surv. 51, 93:193:42 (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, 7485 (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, 1221 (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, 169181 (2019).

Article Google Scholar

le Maire, G. et al. MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. Remote Sens. Environ. 115, 26132625 (2011).

Article ADS Google Scholar

Ricotta, C. & Di Vito, S. Modeling the Landscape drivers of fire recurrence in Sardinia (Italy). Environ. Manage. 53, 10771084 (2014).

Article ADS Google Scholar

Rodrigues, M. & de la Riva, J. An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environ. Model. Softw. 57, 192201 (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, 6374 (2014).

Article Google Scholar

Vilar del Hoyo, L., Martn Isabel, M. P. & Martnez 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, 983996 (2011).

Breiman, L. Random forests. Mach. Learn. 45, 532 (2001).

Article Google Scholar

Barredo Arrieta, A. et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82115 (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.) 127145 (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, 488518 (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 ADS Google Scholar

Kaufman, S., Rosset, S., Perlich, C. & Stitelman, O. Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data 6, 15:115:21 (2012).

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

Altmann, A., Toloi, L., Sander, O. & Lengauer, T. Permutation importance: A corrected feature importance measure. Bioinformatics 26, 13401347 (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 92699278 (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.) 1738 (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 ADS 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, 851854 (2014).

CAS Article ADS 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-Garca, C. Spatial stratification of wildfire drivers towards enhanced definition of large-fire regime zoning and fire seasons. Sci. Total Environ. 689, 634644 (2019).

CAS Article ADS 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, 117129 (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, 785796 (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, 177186 (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, 878888 (2012).

Article Google Scholar

Ganteaume, A. et al. A review of the main driving factors of forest fire ignition over Europe. Environ. Manage. 51, 651662 (2013).

Article ADS 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, 341354 (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, 4356 (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, 935948 (2018).

Article Google Scholar

Conedera, M. et al. Characterizing Alpine pyrogeography from fire statistics. Appl. Geogr. 98, 8799 (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, 543548 (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).

DEste, 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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Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe | Scientific Reports -...