| Abstract: | In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN)
method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage,
current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two
neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the
current flow). The coefficient of determination (R
2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously
measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of
delayed precipitation, 3 days of delayed flows, and temperature of the current. The R
2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation
because they accounted for more input variables. In all models, the variables with the highest R
2 ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature. |
