A Comparative Study of Using Adaptive Neural Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), and SMRGT Models in Flow Coefficient Estimation

Abstract

Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow. This study aimed to predict the flow coefficient for a period of 19 years (2000-2019) in the Aksu River Sub-Basin in Turkey, using historical climatic data, including precipitation, temperature, and humidity, provided by The Turkish State of Meteorological Service (TSMS). The study utilized three different approaches, namely, the Adaptive Neural Fuzzy Inference System (ANFIS), Simple Membership function and fuzzy Rules Generation Technique (SMRGT), and Gaussian Process Regression (GPR), to predict the flow coefficient. The models were evaluated using several statistical tests, such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), and Mean Square Error (MSE), to determine their accuracy. Based on the evaluation criteria, it is concluded that the Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model has superior flow coefficient estimation performance than the other models.