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A study on the simulation of rainfall-runoff process using Artificial Neural Network (ANN) and HEC-HMS (Case study: Kasilian Basin)
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Vahid Gholami *   , Zahra Darvari |
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Abstract: (13756 Views) |
| HEC-HMS and Artificial Neural Network (ANN) were applied to simulate rainfall- runoff process in Kasilian Basin where is located in the north of Iran with an area of 68 km2. ANN has high capability in establishing connection between input and output data and HEC-HMS model has high capability in optimizing simulated hydrograph. Initial Loss is a quantitative parameter which is dependent on three main factors including: soil, vegetation and Antecedent Moisture Conditions(A.M.C). In this study after optimizing initial loss using HEC-HMS model, this parameter along with incremental rainfall were applied qua inputs in ANN to simulate runoff or discharge values. Comparison of the obtained results using ANN (two cases: using optimized initial loss and without optimized initial loss in simulating runoff-rainfall process) showed that optimized initial loss has a high effect in increasing (twice in some events) the simulation accuracy of run off and flood hydrograph. |
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| Keywords: ANN, HEC-HMS, Initial Loss, Kasilian Basin and Rainfall- runoff simulation. |
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Full-Text [PDF 149 kb]
(2555 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2014/03/2 | Accepted: 2014/03/2 | Published: 2014/03/2
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