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مدلسازی بار رسوب رودخانه سودجان با استفاده از رگرسیون چند متغیره سامانه ی فازی و ارزیابی فاکتور فرسایش پذیری خاک در حوزه آبخیز سد زاینده رودعلیا

نوع محتوی: طرح پژوهشی
Language: Persian
Document National Code: R-1278204
Publish: 28 September 2021
دسته بندی علمی: مهندسی آب و هیدرولوژی
View: 464
Pages: 141
Publish Year: 1389

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مدلسازی بار رسوب رودخانه سودجان با استفاده از رگرسیون چند متغیره سامانه ی فازی و ارزیابی فاکتور فرسایش پذیری خاک در حوزه آبخیز سد زاینده رودعلیا abstract

One of the pivotal factors of the Universal Soil Loss equation which plays a key role in measuring the amount of eroded soil is the soil erodibility factor that its value is determined using Wischmeier’s Nomograph. The estimated amount of sedimentation in a watershed, especially in the study area is of vital importance
This study was conducted to:
a- Investigate the efficiency of soil erodibility factor and its performance in this region,
b- Model the suspended sediment load using multiple regression, least squares fuzzy regression, and fuzzy inference system.
In the first stage, overlaying the maps of geology, physiography, vegetation cover, and geomorphic land form (i.e. percentage of slope and elevation).
To observe real soil erodibility factor through the rainfall-runoff event, amount of suspended sediment load at the output of the basin were sampled directly by using integration sampling method, and converted to amount of soil erosion using sediment delivery ratio. The results indicated that Wischmeier’s Nomograph estimated the soil erodibility factor about 2.14 times greater than calculated soil erodibility factor (derived from observed sediment load). Therefore the USLE will be 2.14 times more than real erosion rate. So it can be implied that modification of this coefficient is essential in case of model application.
Second part of the study focuses on an appropriate modelling of suspended sediment load as dependent variable against pedologic and hydrologic properties of homogenous units as independent variables. The modelling sediment load used classic regression, least squares fuzzy regression, and fuzzy inference system. Results showed that in classic regression, least squares fuzzy regression, and fuzzy inference system, there is high correlation coefficient between independent variables include: slope, slit, Caco3 percent, and Curve number and dependent variable (Sediment load).

مدلسازی بار رسوب رودخانه سودجان با استفاده از رگرسیون چند متغیره سامانه ی فازی و ارزیابی فاکتور فرسایش پذیری خاک در حوزه آبخیز سد زاینده رودعلیا authors