Modeling the relationship between rainfall and runoff is an important issue in hydrology, but it is a complicated task because both the high levels of complexity in which both processes are embedded and the associated uncertainty, affect the forecasting. Neuro-fuzzy models have emerged as a useful approach, given the ability of neural networks to optimize parameters in a fuzzy system. In this work a Self-Identification Neuro-Fuzzy Inference Model (SINFIM) for modeling the relationship between rainfall and runoff on a Chilean watershed is proposed to reduce the uncertainty of selecting both the rainfall and runoff lags and the number of membership functions required in a fuzzy system. The data comes from the Diguillín river located in Ñuble region and average daily runoff and average daily rainfall recorded from years 2000 to 2018, according to the Chilean directorate of water resources (DGA). In addition, we worked with the Colorado River basin, located in the Maule region, to validate the method developed. The experimental results showed a good adjustment using the last 3 years as validation set, further improvement was achieved using only the last year was used as validation test, obtaining 84% of $R^2$ and $0,91$ Kling Gupta Efficiency, higher than other forecasting models such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial neural networks (ANN), and Long Short-Term Memory (LSTM) approach. In addition, Nash-Sutcliffe efficiency and percent BIAS indicate the method is a promising model. On the other hand, even better results were obtained in the validation basin, whose adjustment was 94% and an efficiency of 97%. Therefore, the proposed model is a solid alternative to forecast the runoff in a given watershed, obtaining good performance measurements, managing to predict both the low and peak runoff values from rainfall events, avoiding the requirement to determine a priori the lags of time series and the number of fuzzy rules.

对降雨和径流之间的关系进行建模是水文学中的重要问题，但这是一项复杂的任务，因为嵌入这两个过程的高度复杂性以及相关的不确定性都会影响预测。鉴于神经网络能够优化模糊系统中的参数，神经模糊模型已经成为一种有用的方法。在这项工作中，提出了一种用于对智利流域的降雨和径流之间的关系进行建模的自识别神经模糊推理模型（SINFIM），以减少选择降雨和径流滞后的不确定性以及在一个流域中所需的隶属函数的数量。模糊系统。数据来自üuble地区的Diguillín河，并记录了2000年至2018年的平均每日径流量和平均每日降雨量，据智利水资源局（DGA）称。此外，我们与位于莫勒地区的科罗拉多河流域合作，以验证所开发的方法。实验结果表明，使用最近3年作为验证集可以很好地进行调整，仅使用去年作为验证测试​​就可以实现进一步的改进，获得84％${\mathrm{[R}}^2$ 和 $0，91$Kling Gupta的效率高于其他预测模型，例如自适应神经模糊推理系统（ANFIS），人工神经网络（ANN）和长短期记忆（LSTM）方法。另外，Nash-Sutcliffe效率和BIAS百分比表明该方法是一种很有前途的模型。另一方面，在验证盆中获得了更好的结果，其调整率为94％，效率为97％。因此，所提出的模型是预测给定流域内径流量，获得良好性能测量值，设法预测降雨事件的低径流峰值和峰值径流值的可靠替代方案，从而无需事先确定时间序列和模糊规则的数量。