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Although hybrid artificial neural network (ANN) models optimized by metaheuristic algorithms are increasingly used in susceptibility mapping, they are often evaluated without strong machine learning benchmarks, spatially robust validation, or statistical significance testing. This study benchmarks two practically parameter\u2010free evolutionary ANN models, Bernstein\u2013Levy Differential Evolution ANN (BDE\u2010ANN) and Multi\u2010Population Differential Evolution ANN (MDE\u2010ANN), against Logistic Regression, Support Vector Machine with RBF kernel, Random Forest, XGBoost, ANN, and standard DE\u2010ANN. A balanced spatial dataset consisting of 272 wildfire and 272 non\u2010wildfire locations in \u00c7anakkale, T\u00fcrkiye, was constructed using 14 geo\u2010environmental conditioning factors. After multicollinearity assessment and ablation analysis, average temperature was excluded, resulting in a final set of 13 predictors. To reduce optimistic bias caused by spatial autocorrelation, model performance was evaluated using spatial block cross\u2010validation. Predictive uncertainty and pairwise model differences were further assessed through bootstrap AUC confidence intervals, Wilcoxon signed\u2010rank tests, and McNemar tests. The results showed that MDE\u2010ANN achieved the highest overall discrimination and lowest prediction error (Test AUC\u2009=\u20090.874\u2009\u00b1\u20090.064; MSE\u2009=\u20090.120\u2009\u00b1\u20090.041). However, its advantage over Random Forest and XGBoost was not statistically significant, indicating that MDE\u2010ANN should be interpreted as a top\u2010tier but not universally dominant classifier. In contrast, BDE\u2010ANN provided the highest recall (0.985\u2009\u00b1\u20090.030) and F1\u2010score (0.842\u2009\u00b1\u20090.052), making it particularly suitable for recall\u2010priority screening where missed fire\u2010prone areas are highly undesirable. RF and XGBoost offered highly competitive performance with substantially lower computational cost. Overall, the findings support a task\u2010oriented WSM framework in which MDE\u2010ANN is preferable for balanced risk discrimination, BDE\u2010ANN for fire\u2010detection\u2010oriented screening, and tree\u2010based ensembles for rapid baseline deployment.<\/jats:p>","DOI":"10.1111\/tgis.70318","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T08:21:23Z","timestamp":1781338883000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Artificial Neural Network Performance for Wildfire Susceptibility Mapping Using Bernstein\u2010Levy and Multi\u2010Population Differential Evolution Algorithms"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6273-9298","authenticated-orcid":false,"given":"Talha","family":"Ta\u015fkanat","sequence":"first","affiliation":[{"name":"Department of Geomatics Engineering, Engineering Faculty Erciyes University  Kayseri T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,13]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"Adab H. 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