Artificial Neural Networks Potential in Multi-Station Modeling of Suspended Load in Comparsion with Sediment Rating Curve Method

Document Type : Research Paper

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Abstract

Sediments transported by river may cause damages to cultivated land and hydraulic structures. Accurate estimation of sediment load for hydraulic structures (e.g. dam) can prevent extra costs. Because of the existence of many rivers, our country, Iran, has high potential for dam construction. On the other hand, flood disaster causes huge damage every year. The main reason for magnifying the effects of this disaster can be related to the reduction of  water conveyance capacity of the rivers because of sediment deposition. Therefore, the correct estimation of the transported sediment will be highly important. Prediction of the suspended sediment load can be accomplished by the Artificial Neural Networks (ANNs). In this study, ANNs are used to estimate suspended sediment load in Akhola station, located on the AjichayRiver in East Azarbaijan, Iran. The available data for this station were daily discharge and sediment load The ANN sensivity for these parameters was examined in the modeling. In order to evaluate the effect of the upstream stations load, the data of Markid and Vanyar stations were also used to train the network, which led to more accurate result. The classic rating curve method was also used to estimate the sediment load at this station. To optimize the coefficients of the rating curve, the genetic algorithm was employed, its result of caerse did not show superiority on the classic optimization method. Regarding these results, multi-station estimation using ANNs has better efficiency.

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اعلمی م ت، حسن­زاده ی، فاخری فرد ا، 1381. شبیه سازی رسوبگذاری در مخازن سدهای ذخیره ای با استفاده از مدل لوله جریان، مجلة دانشکده فنی دانشگاه تبریز، جلد 28، شماره 3، 9-1.
شفاعی بجستان م، 1384. هیدرولیک رسوب.  انتشارات دانشگاه شهید چمران (اهواز)، چاپ سوم.
منتظر غ، ذاکر مشفق م و قدسیان م، 1381. تخمین خبره رسوب رودخانه بازفت به کمک شبکه عصبی مصنوعی. ششمین کنفرانس بین المللی مهندسی رودخانه، دانشگاه شهید چمران اهواز، 8-1.
منهاج م­ب، 1384. مبانی شبکه های عصبی (هوش محاسباتی). انتشارات دانشگاه صنعتی امیرکبیر، چاپ سوم.
مهدی زاده م­ب، 1383. شبکه های عصبی مصنوعی وکاربرد آن در مهندسی عمران. انتشارات عبادی، چاپ اول.
Agarwal A, Singh RD and Bhunya PK, 2005. ANN-based sediment yield models for Vamasadhara river basin (India). Water SA 31: 95-100.
Alp M and Cigizoglu HK, 2005. Suspended sediment load simulation by two artificial neural network methods using hydro meteorological data. J. of Environmental Modeling and Software 22: 2-13.
Cigizoglu HK and Alp M, 2006. Generalized regression neural network in modeling river sediment yield. J. of Advances in Engineering Software 37: 63-68.
Cigizoglu HK and Kisi O, 2006. Methods to improve the neural network performance in suspended sediment estimation. J. of Hydrology 317: 221-238.
JianSK, 2001. Development of integrated sediment rating curves using ANNs. J of Hydraulic Engineering 127-1: 30-37.
Lin B and Namin MN 2005. Modeling suspended sediment transport using an integrated numerical and ANNs. J. of Hydraulic Research 43-3: 302-310.
RaghuwanshiNS, Singh R and Reddy LS, 2006. Runoff and sediment yield modeling using artificial neural network: upper Siwane River, India. J. of Hydrologic Engineering 11-1: 71-79.
Sarangi A and BhattacharyaAK, 2005. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. J of Agricultural Water Management 78: 195-208.
 
Water and Soil Science
Volume 19, Issue 2
May 2009
Pages 45-55

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