دانشگاه تهرانمحیط شناسی1025-862040420141222Environmental Impact Assessment using Fuzzy logic inference model
(Case study: Kamal Saleh Damارزیابی آثار محیطزیستی با استفاده از مدل استنتاج منطق فازی (مطالعۀ موردی: سد کمال صالح)9739885301310.22059/jes.2014.53013FAوحید فرامرزیکارشناس ارشد محیطزیست، دانشکدۀ منابع طبیعی، دانشگاه صنعتی اصفهانعلیرضا سفیانیاندانشیار گروه محیطزیست، دانشکدۀ منابع طبیعی، دانشگاه صنعتی اصفهان201401200.36), low (L: 0. 36≥X>0.52), medium (M: 0.52≥X>0.68), high (H: 0.68≥X>0.84) and very high (VH: 0.84≥X>1). Fuzzy logic study method In the fuzzy logic method, above mentioned mathematical matrix indices were considered as fuzzy inference system's input. Criteria got fuzzificationed and after determination of membership functions similar to the groups of mathematical matrix classification, and forming rule base center the importance of impact calculated by using the center of gravity method as Defuzzzification approach. The output of the fuzzy logic inference actually is the effect of each activity on the environment and ultimately, the efficiency of two mentioned methods was compared for assessment of effect importance. These two methods have quite similar inputs and finally classified outputs which actually is the importance of the impact, were compared. To do this, in mathematical matrix method and fuzzy logic, 6 criteria for 2 indices (complementary index & basic index) were used (magnitude (Mij), duration (Dij) & occurrence Time (Tij) as basic index parameters, and synergy effects (Sij), cumulative effects (Aij) & probability of occurrence (Pij) as complementary index parameters). In the method of fuzzy inference system using Matlab Ver R2012a software and applying Mamadani implication method and use the same mathematical matrix indices as system input was implemented. Discussion of Results and Conclusions According to the below chart review (Fig.1), the difference in the number of linguistic variables in mathematical matrix and fuzzy methods is obvious. These differences arise from the decision making method in Aristotelian logic and fuzzy logic. In mathematical matrix if the number is placed in border area (high or low range), still belongs to the same range. The importance of the impact calculated based on a mathematical matrix class can create Uncertainty, which is more important in borders of classification (where X is increasing along with the value of impact from very low to very high). i.e. as we move towards increasing the variable X, the value of linguistic variable have increased. This can be seen as several classes in output matrix. For example, if the variable is X=0.53, belongs to medium-class and if variable is X=0.67, still belongs to the same class, even though there has been a major numerical increase; on the other hand, with the increase of 0.01 at 0.67 point, the importance of impact will change from medium to high. Figure 1. Comparing the numeric summation of whole impacts importance (positive and negative) in mathematical matrix and fuzzy logic methods But fuzzy logic approach solved this problem and its output defined based on membership grade. For example, if the output of fuzzy logic is 0.67, then the fuzzy logic determines a degree of membership for two membership functions, and thus the uncertainty in the mathematical matrix classification, which is acting as a binary logic, would improve. Impact importance of Ȳ=0.67 in fuzzy logic belongs to two membership functions with different membership levels, moderate linguistic variable with 0.06 degree of membership and high linguistic variable with 0.94 membership degree. The concept of environmental impact Assessment is unambiguous and ecological effects cannot be explicitly defined, for this reason the fuzzy logic has a very high performance in formulating the importance of each impact in an appropriate manner. Fuzzy logic is capable of using qualitative criteria or linguistic variables for assessment and solves the problem of the variables formulation and simultaneously is capable to use and synthesis both qualitative and quantitative data derived from environmental assessors. As a result, the fuzzy logic method leads to modification of uncertainty which always is a problem in unambiguous and complicated matters such as EIA. Since one of the main issues in environmental impact assessment (regarding project approval and determination of appropriate corrective solutions) is to define the impact significance correctly; the fuzzy logic with its spectacular capabilities is an appropriate method. Determining the importance of environmental impacts is one of the main issues in the process of environmental impact assessment (EIA). Ecological impacts assessment is very complicated and always associated with uncertainty because the assessment data are often qualitative and common EIA methods are incapable of managing these kind of data.]]>https://jes.ut.ac.ir/article_53013_c6d047279865967a3360905d55cb9c6b.pdf