Statistical Functions as an Auxiliary Means in Hydrological Time Series Modeling Using New Data Driven Techniques
Publish place: 8th International Congress on Civil Engineering
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICCE08_536
تاریخ نمایه سازی: 28 آبان 1387
Abstract:
Interesting in using of relatively new data driven modeling techniques (eg. neural networks or neurofuzzy) is being increased among water resources engineering researchers in recent years. One of difficult task in hydrological modeling using these new computational intelligence techniques is selecting appropriate number of model inputs. In this study, the ability of using auto-, cross-, and partial autocorrelation functions were evaluated for model input selection. The method was applied for Sembrong dams reservoir inflow data located in Malaysia. The selected inputs were used to construct simple neural network and neuro-fuzzy models. Results show using these statistical functions can reduce attempts in modeling hydrological time series using computational intelligence techniques.
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Authors
Shahram Karimi-Googhari
Assistant Professor, Department of Water Engineering, Shahid-Bahonar University of Kerman, Kerman, Iran
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