4914

4617

كارشناسي ارشد

محيط زيست

اصفهان: دانشگاه صنعتي اصفهان، دانشكده عمران

1388

هفده،113ص: مصور، جدول، نمودار

ص.ع. به: فارسي و انگليسي

مجيد سرتاج، كيوان اصغري

20/10/88

ناصر طالب بيدختي، نادر فتحيان پور

1396/09/22

كتابنامه

مهندسي عمران

ID4617

به فارسي و انگليسي: قابل رويت در نسخه ديجيتال

Investigation of Fate and Transport of Contaminants in rivers Using Artifical Neural Network Case Study zayandeh Rood River Hojjat Rahimi h rahimi@cv iut ac ir 1 July 2009 Department of Civil Engineering Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language FarsiDr Majid Sartaj msartaj@cc iut ac irAbstractEstimation and prediction of the extent of surface water resources pollution due to municipal industrialand agricultural activities prevention of pollution of these resources and funding of monitoringprograms are some of the main issues in water resources management Many rivers are the main waterresource for drinking agriculture and industry and variation of their quality is of special importance Considering the above estimation and prediction of river water quality parameters along the rivers isone of the main objectives of managers and decision makers in the filed of water resourcesmanagement Many models have been established to simulate river water quality Most of these modelsneed extensive input parameters such as climatological and hydrological data as well as hydraulic inputvariables such as velocity and cross section of rivers which are difficult or costly to obtain Qual2e WASP and HSPF are some examples of such models Artificial neural networks ANN has been investigated in this research as a prediction tool to estimateriver water quality One of the main advantages of using ANN is their capability to solve nonlinearproblems In this research ANN was used to develop a model which incorporates existing water qualitymeasurements for TDS EC BOD Cl pH HCO3 TH at monitoring stations of Zayande Rud river inorder to predict the amount of these parameters in other stations and future time The input parametersincluded flow rate temperature and precipitation and water quality parameters Results showed thatANN is capable of predicting the water quality of the river Considering NMSE MAE and R2 asmeasures of precision of model ANN performed very well for TDS EC and BOD and relatively wellfor Cl pH HCO3 TH NMSE and R2 for TDS EC and BOD were in the range of 0 62 0 92 and0 06 0 54 respectively The prediction model for TDS had the best performance with NMSE of 0 06 0 23 and R2 0 89 0 93 The prediction model for pH had the least desirable performance with NMSE of0 10 0 45 and R2 0 63 0 90 Results obtained by ANN were compared with those obtained by linearand nonlinear regression models Comparison of the results clearly shows the superiority of ANN NMSE and R2 for linear and nonlinear regression models were in the range of 0 42 2 91 and 0 30 0 63 respectively compared to the values of 0 06 0 39 and 0 68 0 92 for ANN results

مجيد سرتاج، كيوان اصغري

ناصر طالب بيدختي، نادر فتحيان پور