The ability of the adaptive neuro-fuzzy inference algorithm architecture to simulate floods is explored in this research. The development of models for flood forecasting has been centered on two adaptive neuro-fuzzy inference (ANFIS) algorithms. The Takagi–Sugeno fuzzy inference systems (FIS) generated through subtracted clustering were trained using hybrid and backpropagation training algorithms. Multiple statistical performance evaluators were used to assess the performability of the established models. The validity and predictive power of the models are evaluated by estimating a flood occurrence in the study area. In designing the models, a total of 12 inputs were employed. The best performability was found for the ANFIS model created utilizing a hybrid training algorithm with mean square error (MSE) of 0.00034, co-efficient of correlation (R²) of 97.066%, root mean square error (RMSE) of 0.018, Nash–Sutcliffe model efficiency (NSE) of 0.968, mean absolute error (MAE) of 0.0073 and combined accuracy (CA) of 0.018, indicating the possible usage of exploiting the established model for prediction of floods.

这项研究探索了自适应神经模糊推理算法体系结构模拟洪水的能力。洪水预报模型的开发已经集中在两种自适应神经模糊推理（ANFIS）算法上。通过混合聚类和反向传播训练算法对通过减法聚类生成的Takagi-Sugeno模糊推理系统（FIS）进行了训练。使用多个统计性能评估器来评估已建立模型的性能。通过估计研究区域的洪水发生率来评估模型的有效性和预测能力。在设计模型时，总共使用了12个输入。对于使用混合训练算法（平均均方误差（MSE）为0.00034，相关系数为（R ²）为97.066％，均方根误差（RMSE）为0.018，纳什–苏特克利夫模型效率（NSE）为0.968，平均绝对误差（MAE）为0.0073，组合精度（CA）为0.018，表明可能使用利用已建立的洪水预报模型。