A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly streamflow data for a river from the annual streamflow data. To develop the proposed hybrid model, a combination of K-Nearest Neighbor (KNN) and Random Tree (RT) algorithms was used. The hybrid structure was designed to predict the annual flow data for the target river using the annual flow data from neighboring rivers by applying the KNN model, and then generated the monthly flow data for the target river using the predicted annual flow by applying the random tree model. The hybrid model was applied to three rivers in Iraq. The accuracy of the proposed model was tested using two statistical indices, namely, the degree of determination and the efficiency coefficient. The results of the statistical indices indicated a good performance of the proposed hybrid model to generate monthly streamflow using annual streamflow data that the values of degree of determination and efficiency coefficient were greater than 0.91 in the training phase and greater than 0.79 in the validating phase.