ABSTRACT High-accuracy flood susceptibility maps play a crucial role in flood vulnerability assessment and risk mitigation. This study assesses the potential application of three new ensemble models, which are integrations of the adaptive neuro-fuzzy inference system (ANFIS), analytic hierarchy process (AHP), certainty factor (CF) and weight of evidence (WoE). The experimental area is the Trotuș River basin in Romania. The database for the present research consisted of 12 flood-related factors and 172 flood locations. The quality of the models was evaluated using root mean square error (RMSE) values and the ROC curve (AUC). The results showed that the ANFIS-CF model and the ANFIS-WOE model have a high prediction capacity (accuracy > 91.6%). Therefore, we concluded that ANFIS-CF and ANFIS-WoE are two new tools that should be considered for future studies related to flood susceptibility modelling.

中文翻译：

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新的基于神经模糊的机器学习集成，用于提高洪水敏感性绘图的预测精度

摘要 高精度洪水敏感性图在洪水脆弱性评估和风险缓解中起着至关重要的作用。本研究评估了三种新集成模型的潜在应用，它们是自适应神经模糊推理系统 (ANFIS)、层次分析过程 (AHP)、确定性因子 (CF) 和证据权重 (WoE) 的集成。试验区是罗马尼亚的 Trotuș 河流域。本研究的数据库包括 12 个与洪水相关的因素和 172 个洪水地点。使用均方根误差 (RMSE) 值和 ROC 曲线 (AUC) 评估模型的质量。结果表明，ANFIS-CF模型和ANFIS-WOE模型具有较高的预测能力（准确率>91.6%）。所以，