River flow forecasting using artificial neural networks
Publish Year: 1383
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,984
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
HDRS_49
تاریخ نمایه سازی: 16 آذر 1384
Abstract:
River flowforecasting is required to provide important information on a wide range of cases related to design and operation of river systems. Since there are a lot of parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically-based models is often a difficult and time consuming procedure. So it is preferred to implement a heuristic black box model to perform a non-linear mapping between the input and output spaces without detailed consideration of the internal structure of the physical process.The base of intelligent methods is to use the inner knowledge of data, extraction of native relationships between them and generalization in other locations.Artificial Neural Network (ANN) is one of the most popular methods of artificial intelligence that mimics the characteristics of the human brain and saves the information of data in the network weights during the training process. In this study, the capability of ANNs for stream flow forecasting in the Sulaghan river at Kan hydrometric station was investigated. Two types of ANNs namely Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were introduced and implemented. The results show that the discharge can be adequately forecasted by these kinds of ANNs.
Authors
M. Zakermoshfegh
PhD Student of Civil Engineering, Tarbiat Modarres University
M. Ghodsian
Associate Professor of Hydraulic Engineering, Tarbiat Modarres University
Gh.A. Montazer
Assistant Professor of Electrical Engineering, Tarbiat Modarres University
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :