7080

6622

كارشناسي ارشد

محيط زيست

اصفهان: دانشگاه صنعتي اصفهان، دانشكده منابع طبيعي

1390

يازده، 96ص.: مصور، جدول، نمودار

ص.ع. به فارسي و انگليسي

حسين مرادي

حسين بشري

25/7/91

عليرضا سفيانيان، آزاده احمدي

1396/09/20

كتابنامه

منابع طبيعي

مهندسي منابع طبيعي

ID6622

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Application of Bayesian Belief Network and Modified RapidImpact Assessment Matrix to Assess the Environmental Impacts of Rudbar Dam Lorestan Mozhgan Farhadian Email address m farhadian@in iut ac ir Date of Submission 28 2 2012 Department of Natural Resources Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Hossein Moradi hossein moradi@cc iut ac irAbstractDams are being constructed for different purposes such as water reservoir Electricity generation watercontrol for irrigation etc About 60 of major rivers have been blocked by dams and had significantimpacts on the river hydrology and morphology Therefore these changes have been resulted asbiodiversity reduction and destruction and deterioration of different ecosystems In this study we assessedthe impacts of Rudbar dam located in Lorestan province using two methods as Modified Rapid ImpactAssessment Model MRIAM and Bayesian Belief Networks BBNs for both construction and operationphases of the project In MRIAM the impacts are being identified and then it assesses the significance ofthe identified impacts The advantage of the MRIAM compare to Rapid Impact Assessment Model RIAM is in consideration of Environmental susceptibility of affected area which it has improved the RIAM tomore efficient method In the other hand we applied the BBNs to assess the impacts of the Rudbar dam onthe environmental factors in two parts as reservoir river upstream and downstream BBNs are workingbased on the Bayes Rule 1763 and distribute the information among the nodes where an applicablemethod provided helping environmental manager to have better management BBNs are able to gather theheterogeneous information as a node and make the links between the nodes Also BBNs are able toaggregate the information and make a final decision In overall two methods showed that the most of theimpacts are negative Based on MRIAM we found that 81 3 and 65 6 of the impacts negative and18 7 and 34 4 of the impacts are positive in construction and operation phases respectively Based onBBNs we found that about 72 6 and 69 1 of the negative impacts for reservoir river upstream anddownstream respectively are in the High class of significance These high significances can be as theimpacts of large dams are permanent long term and extensive I we compare two methods MRIAM isquicker and easier to handle and can consider many parameters in the analysis but in BBNs we are not ableto consider the parameters as so in MRIAM as well as impacts identification and the linking between themare difficult In the other hand by BBNs can make dynamic networks where we can have differentscenarios of impacts as well as show the indirect and cumulative impacts We conclude that these twomethods can support each other to have a more precise and more comprehensive assessment Keywords Environmental impact assessment Bayesian Belief networks Rapid ImpactAssessment Model Rudbar dam Lorestan

حسين مرادي

حسين بشري

عليرضا سفيانيان، آزاده احمدي