تعیین ضریب فرسایش‌پذیری بین‌شیاری خاک بر اساس سیستم‌های فازی و فازی- ژنتیک در استان آذربایجان‎شرقی

نوع مقاله : مقالات پژوهشی

نویسندگان

• حبیب پالیزوان زند
• عباس احمدی

دانشگاه تبریز

https://doi.org/10.22067/jsw.v30i3.38028

چکیده

در این تحقیق، جهت برآورد ضریب فرسایش‌پذیری بین‌شیاری خاک از روی خصوصیات سهل الوصول از علم منطق فازی و فازی-ژنتیک استفاده شد. بدین ترتیب ابتدا 36 سری خاک با تفرق خصوصیات بالا انتخاب و برخی از خصوصیات آن‌ها از قبیل بافت، ماده آلی، کربنات کلسیم معادل، گچ و ESP، SAR، CEC، EC و pH با روش‌های متداول آزمایشگاهی در سال1387 تعیین گردید. همچنین بعد فراکتالی خاکدانه‌های خاک با استفاده از الک مرطوب و بدون اصلاح شن با استفاده از مدل ریئو و اسپوزیتو محاسبه و ضریب فرسایش‌پذیری بین‎شیاری خاک به کمک دستگاه شبیه‌ساز باران، و اندازه‌گیری شدت تولید رواناب و رسوب تعیین شد. سپس با استفاده از تحلیل آماری متغیرهای درصد شن و بعد فرکتالی خاکدانه‌ها به عنوان ورودی مدل‎ها و متغیر ضریب فرسایش‌پذیری بین‎شیاری به عنوان خروجی مدل انتخاب گردید و متغیرهای کلامی خصوصیات فوق و تابع عضویت آن‌ها تعریف گردید. سپس در سیستم استنتاج ممدانی قوانین مدل نوشته شد. در نهایت خروجی مدل با استفاده از روش میانگین وزنی غیرفازی شد. یک بار دیگر توابع عضویت و وزن‌ قوانین با الگوریتم ژنتیک بهینه گردید و بهینه هر وزن، در سیستم فازی وارد شده و توابع فازی بهینه شده به دست آمد. مقادیر R2 و RMSE و GMER و GSDER برای مدل فازی به ترتیب برابر 63/0، 592755، 31/1 و 38/1 و برای مدل فازی-ژنتیک به ترتیب برابر 70/0، 441942، 10/1 و 04/1 به دست آمد، که حاکی از دقت و کارآیی بالاتر مدل فازی-ژنتیک نسبت به مدل فازی و بیش برآوردی و پخشیدگی نسبتاً زیادتر داده‌های تخمینی مدل فازی نسبت به مدل فازی-ژنتیک می‌باشد.

کلیدواژه‌ها

• بعد فراکتالی خاکدانه‌ها
• درصد شن
• شبیه‌ساز باران
• قوانین مدل

20.1001.1.20084757.1395.30.3.5.9

عنوان مقاله [English]

Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems

نویسندگان [English]

• Habib Palizvan Zand
• abbas ahmadi

University of Tabriz

چکیده [English]

Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki).
Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM), cation exchange capacity (CEC), sodium adsorption ratio (SAR), EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD) were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn) was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11) Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as independent variables for development fuzzy and fuzzy- genetic models. For this reason their linguistic variables were defined and fuzzy models rules were written by Mamdani's fuzzy inference method. Then, the outputs of model defuzzified by centroid method. Once again, generation of membership functions and fuzzy rules base as well as optimization of fuzzy rule bases was performed by genetic algorithm, and the fuzzy functions were determined by optimized weight of membership functions and fuzzy rules.
Results Discussion: Interrill erodibility parameters (Ki) of the examined soils calculated at 3 rainfall rates using are listed in Table 2. The values ranged from 1.03 to 71.79 × 105 kg s m-4, depending on the soil and rainfall intensity. Results showed that the effect of rainfall intensity on Ki turned to be insignificant. This implies that Ki was independent of rainfall intensities. Results showed that the Triangular and Trapezoidal membership functions are better than the other membership functions for linguistic variables which used in this study. The values of R2, RMSE (Root mean square error) and GMER (Geometric mean error ratio) and GSDER (Geometric standard deviation of error ratio) were 0.63, 592755, 1.31 and 1.38 for the fuzzy model, and, 0.70, 441942, 1.10 and 1.044 for the fuzzy- genetic model, respectively. Higher R2 and lower RMSE of the fuzzy – genetic model shows higher accuracy and efficiency of the fuzzy-genetic model. The GSDER criteria shows better matching of the fuzzy- genetic model estimated values with measured values. The GMER criteria shows lower over-estimation of the fuzzy- genetic model than fuzzy model.
Conclusion: Fuzzy and fuzzy-genetic models which were designed with two input variables namely aggregates fractal dimensions and soil sand content, capable to predict of interrill erodibility coefficient of soils with reasonable accuracy. So using of these models for predicting of interrill erodibility is recommended.Optimization of fuzzy rule bases and membership functions weight increased model accuracy. Therefore, using genetic algorithm in developing fuzzy models for prediction of soil erosion rate is recommended.

کلیدواژه‌ها [English]

• Fractal dimension of aggregates
• Model rules
• Rainfallsimulator
• Sand percentage

مراجع

1- Ahmadi A. 2009. Artificial neural networks applicability in erosion and runoff simulation using fractal dimensions. Thesis of Department of Soil Science, Agricultural College, University of Tabriz. P.197 (in Persian with English abstract).

2- Anonymous. 1954. Diagnosis and improvement of saline and alkali soils. Agric.Handbook60. USDA, US Salinity Laboratory Staff. Washington, DC.

3- Asadipour N., Karami M., and Shahinezhad B.2012.Genetic algorithm using fuzzy logic in determining the parameters for optimizing the outflow hydrograph. Journal of Iran Water Research. Vol. 6 (10): 37-45. (in Persian with English abstract).

4- Bower C.A. 1952. Exchangeable cation analysis of saline and alkali soils. Soil Sci. 730: 251-261.

5- Bozorg Haddad O., Khosrowshahi S., Zarezadeh M., and Javan P. 2013. Development of Simulation-Optimization Model for Protection of Flood Areas. Journal of Water and Soil. Vol. 27 (3): 462-471. (in Persian with English abstract).

6- Cai X., McKinney D.C., and Lasdon L.S. 2001. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Adv. Water Resour. 24 (1): 667–676.

7- Chang C.L., Lo S.L., and Yu S.L. 2005. Applying fuzzy theory and genetic algorithm to interpolate precipitation. J Hydrol. 314:92–104.

8- Chen L. 2003. Real coded genetic algorithm optimization of long-term reservoir operation. Environ. Urbanization. 39(5): 1157–1165.

9- Crowe A.M., McClean C.J., and Cresser M.S. 2006. An application of genetic algorithms to the robust estimation of soil organic and mineral fraction densities. Environ. Model. Software. 21: 1503-1507.

10- Eshtehardian E., Afshar A., and Abbasnia R. 2006. A genetic algorithm-based optimizing approach for project time-cost trade-off with uncertain measure. 3th International Project Management Conference, Tehran, Iran. (in Persian with English abstract).

11- Flanagan D.C., and Nearing M.A. 1995. USDA-Water Erosion Prediction Project (WEPP). WEPP Users Summary. NSERL Report, Vol. 10. USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, Indiana.

12- Foster G.R., Flanagan D.C., Nearing M.A., Lane L.J., Risse, L.M., and Finkner S.C. 1995. Chapter 11. Hillslope erosion component. In: Flanagan, D.C., Nearing, M.A. (Eds.), USDA Water Erosion Prediction Project. Hillslope Profile and Watershed Model Documentation. USDA-ARS-NSERL Report a 10. NSERL, West Lafayette, IN, pp. 11.1–11.12.

13- Gee G.W., and Or D. 2002. Particle-size analysis. In: Warren, A.D. (ed.) Methods of Soil Analysis. Part 4. Physical Methods. Soil Sci. Soc. Am. Inc. pp.255-295.

14- Holland J.H. 1975. Adaptation in Natural, and Artificial Systems, Ann Arboor: The University of Michgan Press.

15- Kinnell P.I.A. 1993. Runoff as a factor influencing experimentally determined interrill erodibilities. Aust. J. Soil Res., 31:333–342.

16- Kisi O., Haktanir T., Ardiclioglu M., Ozturk O., Yalcin E., and Uludag S. 2009. Adaptive neuro-fuzzy computing technique for suspended sediment estimation, Advances in Engineering Software, 40:438-444.

17- Lal R. 1988. Soil Erosion Research Methods. Soil and Water Conserv. Soc. ISSS, P.O. Box. 353, 6700 AJ, Wageningen, Netherlands.

18- Liebenow A., Elliot W.J., Laflen J.M., and Kohl K.D. 1990. Interrill erodibility: Collection and analysis of data from cropland soils. Trans. Am. Soc. Agric. Eng., 33:1882-1882.

19- Liu S.h., Butler D., Brazier R., Heathwaite L., and Khu S. 2007. Using genetic algorithm to calibrate a water quality model. Sci. Total Environ. 374: 260-272.

20- Lo C.H., Chan P.T., Wong Y.K., Rad A.B., and Cheung K.L. 2007. Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Applied Soft Computing 7: 554–560.

21- Mitra B., Scott H.D., Dixon J.C., and McKimmey J.M. 1998. Applications of fuzzy logic to the prediction of soil erosion in a large watershed, Journal of Geoderma, 86: 183-209.

22- Nelson D.W., and Sommer L.E. 1982. Total carbon, organic carbon, and organic matter. In: Sparks D.L., Page A.L., Helmke P.A., Loeppert R.H., Soltanpour P.N., Tabatabai M.A., Johnston C.T., and Sumner M.E. (Ed.), Methods of Soil Analysis: Part 3. Chemical and Microbiological Properties. pp. 539–579, Am. Soc. Agron., Madison.

23- Nimmo J.R., and Perkins K.S. 2002. Aggregate stability and Size distribution. In: Warren, A.D. (ed.). Methods of Soil Analysis. Part 4. Physical Methods. pp. 317-328. Soil Sci. Soc. Am. Inc.

24- Rieu M., and Sposito G. 1991. Fractal fragmentation, soil porosity, and soil water properties: II. Application. Soil Sci. Soc. Am. J., 55:1239–1244.

25- Romero C.C., Stroosnijder L., and Baigorria G.A. 2007. Interrill and rill erodibility in the northern Andean Highlands. Catena, 70:105–113.

26- Seyyednezhad Golkhatm N., Sanaeinejad S.H., Ghahraman B., and Rezaee Pazhand H. 2014. Journal of Water and Soil. Vol. 28(1): 1-9. (in Persian with English abstract).

27- Shad R., Mesgari M.S., Abkar A., and Shad A. 2009. Predicting air pollution using fuzzy genetic linear membership kriging in GIS. Comput Environ Urban Syst. 33(6):472–81.

28- Shu C., and Ouarda T.B.M.J. 2008. Regional flood frequency analysis at ungauged sites using the adaptive neurofuzzy inference system. J. Hydrology, 349: 31-43.

29- Tayfur G., Ozdemir S., and Singh V. 2003. Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces, Journal of Advances in Water Resources, 26: 1249–1256.

30- Teshneh lab M., Saffar Pour and Afuni D. 1999. Fuzzy andFuzzy-Genetic Systems. Khaje Nasiroldin Toosi University of Technology Publications, Tehran, Iran, 3th edition, P 256. (in Persian).

31- Tran L.T., Ridgley M.A., Duckstein L., and Sutherland R. 2002. Application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation. Catena 47(3):203–26.

32- Tung C., Hsu S., Liu C.M., and Li Sh.Jr. 2003. Application of the genetic algorithm for optimizing operation rules of the LiYutan reservoir in Taiwan. J. Am. Water Resour. Assoc. 39 (3): 649–657.

33- Vaziri F.1983. Analysis of rainfall records and determination of intensity- duration curves for different regions of Iran. Frist edition. Jahad Daneshgahi Publications, Tehran, Iran (in Persian).