RAINFALL-RUNOFF MODELING: A FUZZY LOGIC APPROACH

Rainfall-Runoff is very complex phenomena and its often required attention for modeling to estimate the runoff. Several methods are available for the rainfall-runoff modeling. The present study undertakes the rainfall-runoff modeling for Hadad rain gauge station of Dharoi sub-basin, Gujarat, India. In this study, the rainfall-runoff modeling has done using Fuzzy logic (FL) technique for the selected station. The dataset collected for the rain gauge stations was divided into different combination of training and testing ratio and the models were developed for Hadad rain gauge station with the selected combinations. Also, in Fuzzy logic, different combinations of rainfall were considered as the inputs to the model, and runoff was considered as the output. Input space partitioning for model structure identification was done by grid partitioning. A hybrid learning algorithm consisting of back-propagation and least-squares estimation was used to train the model for runoff estimation with the triangular membership function. The outputs generated were than compared to the observed outputs with different combinations of training and testing dataset by means of the model evaluation parameters coefficient of correlation (r), coefficient of determination (r2) and discrepancy ratio (D.R) values..