Flow coefficient modeling using the fuzzy SMRGT method compared with anfis and ANN methods: An example of the Aksu River basin

Estimating the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. It is hard to accurately determine the flow coefficient without a good understanding of the river basin's hydrology, climate, topography, and soil characteristics. The latest flow coefficient modeling literature describes several methods. Most of these methods use opaque, non-generalizable methodologies. Therefore, this research employed three distinct methodologies; specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and the Artificial Neural Network (ANN), to achieve its objectives. The Aksu river basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation, temperature, relative humidity, wind speed, land use, slope, and soil properties data tailored to the particular study region. The study analyzed the results using various performance metrics of the model, such as mean absolute error, Nash-Sutcliffe efficiency coefficient, root mean square error, and correlation coefficient. The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values.