M.M. Chari; B. Ghahraman; K. Davary; A. A. Khoshnood Yazdi
Abstract
Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these ...
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Introduction: Water and soil retention curve is one of the most important properties of porous media to obtain in a laboratory retention curve and time associated with errors. For this reason, researchers have proposed techniques that help them to more easily acquired characteristic curve. One of these methods is the use of fractal geometry. Determining the relationship between particle size distribution fractal dimension (DPSD) and fractal dimension retention curve (DSWRC) can be useful. However, the full information of many soil data is not available from the grading curve and only three components (clay, silt and sand) are measured.In recent decades, the use of fractal geometry as a useful tool and a bridge between the physical concept models and experimental parameters have been used.Due to the fact that both the solid phase of soil and soil pore space themselves are relatively similar, each of them can express different fractal characteristics of the soil .
Materials and Methods: This study aims to determine DPSD using data soon found in the soil and creates a relationship between DPSD and DSWRC .To do this selection, 54 samples from Northern Iran and the six classes loam, clay loam, clay loam, sandy clay, silty loam and sandy loam were classified. To get the fractal dimension (DSWRC) Tyler and Wheatcraft (27) retention curve equation was used.Alsothe fractal dimension particle size distribution (DPSD) using equation Tyler and Wheatcraft (28) is obtained.To determine the grading curve in the range of 1 to 1000 micron particle radius of the percentage amounts of clay, silt and sand soil, the method by Skaggs et al (24) using the following equation was used. DPSD developed using gradation curves (Dm1) and three points (sand, silt and clay) (Dm2), respectively. After determining the fractal dimension and fractal dimension retention curve gradation curve, regression relationship between fractal dimension is created.
Results and Discussion: The results showed that the fractal dimension of particle size distributions obtained with both methods were not significantly different from each other. DSWRCwas also using the suction-moisture . The results indicate that all three fractal dimensions related to soil texture and clay content of the soil increases. Linear regression relationships between Dm1 and Dm2 with DSWRC was created using 48 soil samples in order to determine the coefficient of 0.902 and 0.871 . Then, based on relationships obtained from the four methods (1- Dm1 = DSWRC, 2-regression equationswere obtained Dm1, 3- Dm2 = DSWRC and 4. The regression equation obtained Dm2. DSWRC expression was used to express DSWRC. Various models for the determination of soil moisture suction according to statistical indicators normalized root mean square error, mean error, relative error.And mean geometric modeling efficiency was evaluated. The results of all four fractalsare close to each other and in most soils it is consistent with the measured data. Models predict the ability to work well in sandy loam soil fractal models and the predicted measured moisture value is less than the estimated fractal dimension- less than its actual value is the moisture curve.
Conclusions: In this study, the work of Skaggs et al. (24) was used and it was amended by Fooladmand and Sepaskhah (8) grading curve using the percentage of developed sand, silt and clay . The fractal dimension of the particle size distribution was obtained.The fractal dimension particle size of the radius of the particle size of sand, silt and clay were used, respectively.In general, the study of fractals to simulate the effectiveness of retention curve proved successful. And soon it was found that the use of data, such as sand, silt and clay retention curve can be estimated with reasonable accuracy.
A. Haghverdi; B. Ghahraman; A.A. Khoshnood Yazdi; Z. Arabi
Abstract
چکیده
ظرفیت زراعی و پژمردگی دائم مهمترین نقاط پتانسیلی در مدل سازی و مدیریت آب مورد نیاز محصولات کشاورزی می باشند. روش های مستقیم تعیین میزان رطوبت هزینه بر و گران می ...
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چکیده
ظرفیت زراعی و پژمردگی دائم مهمترین نقاط پتانسیلی در مدل سازی و مدیریت آب مورد نیاز محصولات کشاورزی می باشند. روش های مستقیم تعیین میزان رطوبت هزینه بر و گران می باشد. بنابراین استفاده از توابع انتقالی برای تبدیل خصوصیات زودیافت خاک به خصوصیات هیدرولیکی یک راهکار مناسب برای حل این مشکل است. در این پژوهش کارایی مدل های شبکه عصبی مصنوعی (NNs) آموزش داده شده با نمونه های خاک منتج از جریان خروجی چند مرحله ای (NeuroMultistep outflow) و مدل های نزدیک ترین K همسایه (KNN) در اشتقاق توابع انتقالی به منظور تعیین میزان رطوبت در ظرفیت زراعی و پژمردگی دائم برای 122 نمونه خاک از شمال و شمال شرق ایران مورد بررسی قرار گرفت. همچنین تاثیر عوامل ورودی مختلف و نوع داده به کار رفته برای اشتقاق هر دو روش معین شد. نتایج حاصله نشان دادند که در کل روش KNN (027/0RMSE= ) نسبت به NNs (037/0RMSE= ) نتایج بهتری داشت. همچنین می توان گفت که حساسیت مدل های شبکه عصبی به کیفیت و نوع داده های به کار رفته برای آموزش بسیار بالاست و همگن نبودن داده ها باعث کاهش کارایی مدل های شبکه عصبی و افزایش 100 درصدی خطا می شود. همچنین نتایج نشان دادند که در نظر گرفتن خصوصیات هیدرولیکی به عنوان متغیرهای ورودی در شبکه عصبی باعث ارتقاء نتایج مدل سازی می شود.
واژه های کلیدی: نزدیک ترین K همسایه، شبکه های عصبی مصنوعی، توابع انتقالی، ظرفیت زراعی، پژمردگی دائم
R. Moazenzadeh; B. Ghahraman; K. Davary; A.A. Khoshnood Yazdi
Abstract
Soil moisture retention curve (SMRC) is an important soil property which expresses reaction between matric potential and moisture of soil. Direct measurement of soil matric potential and moisture is labour- and time-consuming. In order to prevail this problem, indirect methods are used for SMRC prediction. ...
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Soil moisture retention curve (SMRC) is an important soil property which expresses reaction between matric potential and moisture of soil. Direct measurement of soil matric potential and moisture is labour- and time-consuming. In order to prevail this problem, indirect methods are used for SMRC prediction. Pedotransfer functions (PTFs) are one of these indirect methods. This study was carried out to evaluate three internal pedotransfer functions, first and second models of Ghorbani and Homaee (1381) and Sepaskhah and Bondar (2002) derived in Iran, to predict SMRC in some Iranian soils. Also we tried to develop new different PTFs with better performance using the available information. Therefore 42 soil samples with spatial distribution from northern region of Iran, Amol, Babol and Karaj were selected and divided in Loam (20 samples) and Clay Loam (22 samples) texture classes. In evaluation of all existing PTFs, all 42 soil samples, and in developing new PTFs, 36 soil samples were used. The remaining six samples (three samples in each texture class) were used for validation of the new developed PTFs. In evaluation of the existing PTFs, results showed that the first and second models of Ghorbani and Homaee had alike and appropriate prediction of moisture in whole range of matric potential, whereas Sepaskhah and Bondar did not show an appropriate prediction. By the way, none of these PTFS had noticeable preference in specific texture classes in comparison with the others. New developed PTFS were highly significant (p
R. Moazenzadeh; B. Ghahraman; F. Fathalian; A.A. Khoshnood Yazdi
Abstract
Abstract
Pedotransfer functions (PTFS) are useful means of prediction many properties of the soil, and especially the hydraulic characteristics of this porous media. The main advantages of this functions, as compare to conventional methods used to directly estimate soil hydraulic properties, is that ...
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Abstract
Pedotransfer functions (PTFS) are useful means of prediction many properties of the soil, and especially the hydraulic characteristics of this porous media. The main advantages of this functions, as compare to conventional methods used to directly estimate soil hydraulic properties, is that they are not time-cost consuming. Different approaches such as classic linear and non linear regressions, artificial neural networks and regressions tree are being employed to develop the PTFS. Rosetta is a software package to predict soil hydraulic properties making use of artificial neural networks- based PTFS. In the present study, the impacts of the type and count of input variables to this software, on the prediction of the moisture retention curve and saturated hydraulic conductivity were evaluated in some soils from northern region of Iran, classed as of Loam and Clay Loam textures (USDA). Our results indicated that addition of bulk density as input variable decreased the performance of moisture retention curve prediction in both textural classes. Addition of bulk density showed on RMSE, ME, GMER and GSDER a positive and negative effect in Loam and Clay Loam textures, respectively. Addition of one or two moisture retention point(s) (the moisture content at matric potential of -33 and -1500 kpa) significantly decreased the RMSE at the medium range of matric potential (i.e. -33 to -500 kpa) and especially at -33 kpa. All of the studied PTFS tended to underestimate both saturated hydraulic conductivity and moisture content at different matric potential.
Key words: Pedotransfer Functions, Hydraulic properties, Moisture retention curve, Saturated hydraulic conductivity, Rosetta, Iran
