اشتقاق توابع انتقالی برای برآورد هدایت هیدرولیکی اشباع خاک در شمال‌غرب دریاچه ارومیه

هدایت هیدرولیکی اشباع خاک (ks) در اکثر مدل های شبیه سازی فرسایش و فرایندهای هیدرولوژیکی خاک در آبخیزها، نقش مهمی را ایفا می کند. اندازه گیری مستقیم هدایت هیدرولیکی اشباع (ks) خاک، کاری وقت گیر، دشوار و پرهزینه است. هدف از این پژوهش، مقایسه ی دقت توابع انتقالی (ptfs) رگرسیونی، شبکه عصبی مصنوعی (ann) و نروفازی در برآورد ks خاک های متاثر از نمک واقع در شمال غرب دریاچه ی ارومیه بود. برای تعیین برخی متغیرهای فیزیکی و شیمیایی زودیافت خاک، تعداد 100 نمونه خاک دست خورده و دست نخورده از عمق 0 تا 10 سانتی متری اراضی کشاورزی و بایر بخش شندآباد در منطقه ی شبستر برداشته شد. متغیر ks، در آزمایشگاه به روش بار ثابت یا افتان اندازه گیری شد. برای اشتقاق توابع رگرسیونی از نرم افزار spss استفاده شد و برای توابع ann و نروفازی از نرم افزار matlab. هشتاد درصد داده ها برای آموزش و بیست درصد آن برای آزمون توابع به کار رفت. نتایج توابع رگرسیونی، ann و نروفازی نشان داد که تابع انتقالی با دو متغیر سیلت و جرم مخصوص ظاهری، بهترین تابع برای برآورد ks خاک در منطقه ی مورد مطالعه است. مقادیر ضریب تبیین (r^2)، مجذور میانگین مربعات خطا (rmse) و میانگین خطا (me) به ترتیب 0.65، cm/min 0.119 وcm/min 0.059 و 0.73، 0.087 cm/min وcm/min 0.006 و 0.69،cm/min 0.127 و cm/min 0.051 به ترتیب برای بهترین تابع رگرسیونی، ann و نروفازی به دست آمد. بنابراین، توابع ann به دلیل داشتن r2 بالا و rmse پایین در مقایسه با توابع رگرسیونی و نروفازی، دقت بیشتری برای برآورد ks خاک در منطقه ی مورد مطالعه دارد.

Deriving Pedotransfer Functions for Estimating Soil Saturated Hydraulic Conductivity in North West of Urmia Lake

Extended abstract1 IntroductionSoil saturated hydraulic conductivity (Ks) is an important factor in the estimation of water, solute transport models and erosion processes. Direct measurement of soil saturated hydraulic conductivity (Ks) in field and laboratory is timeconsuming, laborious and expensive because of high temporal and spatial variability; especially in saltaffected soils around Urmia Lake, Ks measurement is difficult because of high sodium concentration and consequently poor stability of soil aggregates. Therefore, many different regressions, artificial neural network (ANN) and the neurofuzzy pedotransfer functions (PTF) have been developed to estimate Ks from readily available soil variables such as sand, silt, clay, bulk density (BD), particle density (PD), electrical conductivity (EC), pH, and organic carbon (OC). The objectives of this study were to derive pedotransfer functions by using regression, artificial neural network, and neurofuzzy methods to estimate Ks from some soil variables in the saltaffected soils selected from the northwest of Urmia Lake and to compare the performance of the neurofuzzy, artificial neural network and regression models.2 MethodologyDisturbed and undisturbed (steel cylinders with 5 cm diameter and height) soil samples (n= 100) were systematically taken from 010 cm soil depth of bare and agricultural lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Urmia Lake, Iran (45 ° 36ʹ 34ʺ E and 38 ° 6ʹ 37ʺ N). The values of sand, silt, and clay (hydrometer method), CaCO3 (titration method), bulk density (cylinder method), particle density (pycnometer method), organic carbon (wet oxidation method), and total porosity (calculating from BD and PD) were measured in the laboratory. The mean geometric diameter (dg) of soil particles was computed using the percentages of sand, silt, and clay. The EC and sodium adsorption ratio (SAR) were measured in 1:2.5 (soil: distilled water) extra. The pHe was determined in a/the saturated paste. The soil saturated hydraulic conductivity (Ks) was measured by constant (agricultural lands) and falling (bare lands) head method using steel cylinders in the laboratory. The data were divided into two series as 80 data for training and 20 data for testing. The SPSS 18 software with a/the stepwise method to derive the regression PTFs and MATLAB software to derive the artificial neural network and neurofuzzy PTFs were used. A threelayer perceptron network and the tangent sigmoid transfer function were used for the artificial neural network modeling. In estimating soil saturated hydraulic conductivity, the accuracy of neurofuzzy, artificial neural network and regression pedotransfer functions were evaluated by the coefficient of determination (R2), root mean square error (RMSE) and mean error (ME) criteria.3 Results DiscussionMost of the studied soil variables had good distribution for developing and evaluating regression, ANN, and neurofuzzy PTFs. The high values of the coefficient of variation (CV) were found for SAR (167.86%), Ks (130.36%), EC (117.05%), dg (88.44%), clay (73.23%), OC (58.46%) and sand (51.47%) in the studied area. The textural classes of studied soils were loamy sand (n= 3), sandy loam (n= 39), loam (n= 20), silt loam (n= 22), silty clay loam (n= 7), silty clay (n= 7) and clay (n= 2). There were found significant correlations between soil saturated hydraulic conductivity (Ks) and sand (r= 0.60**), silt (r= 0.60**), clay (r= 0.43**), organic carbon (r= 0.36**), bulk density (r= 0.52**), particle density (r= 0.53**), total porosity (r= 0.31**), CaCO3 (r= 0.58**), mean geometric diameter (r= 0.57**), SAR (r= 0.35**), EC (r= 0.22*) and pHe (r= 0.44**). These results are in line with the findings of the former studies that reported direct relation of Ks with OC, sand, and inverse relation of Ks with silt, clay, BD, and SAR. Generally, 8 regression, artificial neural network, and neurofuzzy pedotransfer functions were constructed to estimate soil saturated hydraulic conductivity (Ks) from measured readily available soil variables. The results of the best regression, artificial neural network and neurofuzzy pedotransfer functions indicated that the most suitable input variables to estimate soil saturated hydraulic conductivity (Ks) were bulk density and silt in the studied region. The values of R2, RMSE and ME were obtained equal to 0.65, 0.119 cm min1, 0.059 cm min1 and 0.73, 0.087 cm min1, 0.006 cm min1 and 0.69, 0.127 cm min1, 0.051 cm min1 for the best regression, artificial neural network, and neurofuzzy Ks pedotransfer functions, respectively. According to these results, the ANN PTF was the best in estimating Ks because of having high R2 and low RMSE compared with regression and neurofuzzy PTFs. The former researchers also obtained bulk density and silt as the best input variables for estimating soil saturated hydraulic conductivity (Ks) in different soils and regions.4 ConclusionsThe results showed that bulk density and silt are the most suitable readily available soil variables to estimate soil saturated hydraulic conductivity (Ks) in the studied saltaffected soils. According to the RMSE criterion, the precision of an/the artificial neural networks in estimating Ks was more than regression and neurofuzzy pedotransfer functions in this research. Also, regression and neurofuzzy PTFs have not an/the observable difference in estimating Ks.