A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands

Simulation of the runoff-rainfall process in forest lands is essential for land management. In this research, a hydrologic modelling system (HEC-HMS) and artificial neural network (ANN) were applied to simulate rainfall-runoff (RRP) Kasilian watershed with an area 68 square kilometres. The HMS model was performed using secondary data rainfall discharge at climatology hydrometric stations, Soil Conservation Service (SCS) simulating flow hydrograph, curve number (CN) method runoff estimation, lag time routing. Further, multilayer perceptron (MLP) used process. HEC-HMS optimize initial loss (IL) values as input. IL reflects conditions vegetation, soil infiltration, antecedent moisture condition (AMC) soil. Then, also incremental inputs into ANN values. comparison results RRP two scenarios, without IL, showed that parameter has high effect increasing simulation performance Moreover, predictions more precise those model. can significantly increase decrease generation.