نشریه علمی - پژوهشی مرتع و آبخیزداری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد آبخیزداری، دانشکده منابع طبیعی، دانشگاه گنبد کاووس، ایران.

2 استادیار دانشکده کشاورزی و منابع طبیعی، دانشگاه گنبد کاووس، ایران.

3 استادیار دانشکده کشاورزی منابع طبیعی، دانشگاه گنبد کاووس، ایران.

4 استادیار دانشکده منابع طبیعی، دانشگاه گنبد کاووس، ایران.

چکیده

در بسیاری از مناطق نیمه‌خشک ایران فرسایش خاک به‌عنوان یک معضل محیط‌زیستی بر حاصل‌خیزی خاک، کیفیت آب و زیست‌بوم‌های آبی اثر می‌گذارد. نرخ خاک برداشت شده براساس نوع فرسایش و فرآیندهای تخریب زمین متفاوت است. فرسایش شیاری معمولاً در مواقع بارش شدید بر روی دامنه‌های شیب‌دار ایجاد می‌شود و شرایط انتقال رسوب در آن نامتعادل است. در این تحقیق با استفاده از مدل نروفازی اقدام به شبیه‌سازی غلظت رسوب حاصل از فرسایش شیاری شده است. یک‌سری از روابط تجربی و پارامترهایی که برای شبیه‌سازی هیدرودینامیک شیار، جدا شدن خاک و ظرفیت حمل و انتقال رسوب که بر فرسایش حاصل از شیار مؤثرند به عنوان ورودی مدل در نظر گرفته شدند. فرآیند توسعه و ارزیابی مدل با استفاده از مجموعه داده‌های مشاهده‌ای در 27 شیار آزمایشی با دبی 12 لیتر بر دقیقه مقایسه شد. در این پژوهش برای تعیین ترکیب بهینه ورودی‌ها  از روش گام به گام از میان 10 پارامتر ورودی مؤثر در برآورد غلظت رسوب شامل ویژگی‌های خاک، توپوگرافی و پوشش گیاهی استفاده شد. براساس نتایج روش گام به گام، چهار پارامتر درصد شیب، درصد پوشش گیاهی، درصد رس و تنش برشی جریان برای مدل‌سازی انتخاب شدند. ارزیابی مدل نشان داد که مدل نروفازی با  ضریب تبیین، جذر میانگین مربعات خطا و میانگین خطای اریب، به ترتیب، 697/0، 5/30 و 0/1 قادر به پیش‌بینی قابل قبول غلظت رسوب حاصل از فرسایش شیاری بود. 

کلیدواژه‌ها

عنوان مقاله [English]

Sediment concentration modeling in rill flow using the Adaptive Nero Fuzzy Inference System (ANFIS) in semi arid region

نویسندگان [English]

  • Suma Mohamadpur 1
  • Hamed Rouhani 2
  • Hojat Ghorbani Vaghei 3
  • Seyed Morteza Seyedian 3
  • Abulhasan Fath Abadi 4

1

2

3

4

چکیده [English]

In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed when rainstorms occur on steep slopes and sediment transport in rill flows exhibits the characteristics of non-equilibrium transport. In this paper, sediment concentration of rill flow is estimated by adaptive neuro-fuzzy inference system (ANFIS). A series of mathematical equations and parameters affecting rill hydrodynamics and soil detachment were used for well-defined rill sediment concentration. A series of filed experiments were performed to evaluate the model. The stepwise method was used to select the most important and effective input variables from measured input parameters of soil properties, topographic and vegetation attributes affecting sediment concentration of rill flow. Based on the stepwise procedure, the most significant parameters in the model predications were steep slope, vegetation percentage, clay percentage, and shear stress parameters. The values of sediment concentration simulated by the model were in agreement with observed values with Coefficient of Correlation (R2), Root Mean Square Error (RMSE) and Mean Bias Error (MBE) of 0.697, 30.5 and 1.0, respectively. The results of the investigation shows that the data-driven ANFIS modeling approach can be a powerful alternative technique for correctly estimating rill sediment concentration.

کلیدواژه‌ها [English]

  • Rill erosion
  • Sediment concentration
  • Stepwise method
  • modeling
  • ANFIS
[1].   Ahmadi, H. (1999). Applied Geomorphology, Volume 1 (water erosion), second edition, Tehran. Tehran University Press, 688 pages. (In Persian).
[2].   Ahmadi, H., Jafari, M., Nazari Samani, A., Ghodduosi, J. and Adelpour, A. (2010). Determining hydraulic threshold conditions for gully initiation based on flow simulation. Pajouhesh & Sazandegi, 87, 42-51. (In Persian)
[3].   Ahmadi, H., Tahmoores, M. and Mohamd Asgari, H. (2008). Using fuzzy inference system for the estimation of suspended sediment (Case Study: Taleghan watershed). Watershed Science and Engineering. 2 (5), 53-62. (In Persian)
[4].   Ahmed, S. and Simonovic, S.P. (2004). Spatial system dynamics: New approach for simulation of water resources systems. Journal of Computing in Civil Engineering, ASCE, 18, 331-340.
[5].   Aqhil, M., Yano, A., and Nishiyama, S. (2007). A comparative study of artificial neural network and neuro-fuzzy in continuous modeling of the daily and hourly behavior of runoff. Journal of Hydrology, 33, 22-34.
[6].   Arab Khedri, M. (2005). Study of sediment transport in Iranian watersheds. Iran-Water Resources Research, 1 (2), 51-60. (In Persian).
[7].   Assadi, H., Ruhypuor, H., Refahi, H., Shrfa, M. (2007). Evaluate the WEPP model for estimating interrill erosion in vitro. Iranian Journal of Agricultural Sciences, 38 (4), 563-553. (In Persian)
[8].   Berger, C., Schulze, M., Rieke-Zapp, D. and Schlunegger, F. (2010). Rill development and soil erosion: A laboratory study of slope and rainfall intensity. Earth Surface Processes and Landforms, 35(12), 1456–1467.
[9].   Besalatpour, A.A., Hajabbasi, M.A. and Ayoubi, Sh. (2013). Use of Gamma test technique for choosing the optimum input variables in modeling of soil shear strength using artificial neural networks. J. of Water and Soil Conservation, 20 (1), 97-114.
[1].   Bryan, R. B. (1987). Processes and significance of rill development. Catena, Supplement (8): 1−16.
[2].   Carolloe, F.G., Stefano, C.D., Ferro, V. and Pampalone, V. (2015). Measuring rill erosion at plot scale by a drone-based technology. Hydrol. Process, 29 (17), 3802-3811.
[3].   Castillo, V. M., Gomez-Plaza, A. and Martinez-Mena, M. (2003). The role of antecedent soil water content in the runoff response of semiarid catchments: a simulation approach.  Journal of Hydrology, 284, 114 –130.
[4].   Dabral, P.P. Baithuri, N. And Pandey, A. (2008). Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resources Management, 22, 1783-1798.
[5].   Dehghani, A. A., Asgari, M. and Mosaedi, A. (2009). Comparison of Geostatistics, Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Approaches in Groundwater Level Interpolation (Case study: Ghazvin aquifer). Journal of Agricultural Science and Technology, (Special issue 1-b). 16, 517-536.
[6].   Erfaniyan, H., Ghahremani, P. and Seadat, H. (2013). Potential soil erosion risk maps using fuzzy logic in Golestan watershed Qrnavh. Watershed of Science and Engineering, 7 (23), 43-52. (In Persian)
[7].   Fathabadi, A., and Salajegheh, A. (2009). Estimation of the suspended sediment loud of Karaj River using fuzzy logic and neural network, Journal of Range and Watershed Management, 62 (2), 271-282. (In Persian)
[8].   Fathipour Azar, H., Choupani, N. and Afshin. H. (2011). Estimating fracture energy of concrete (GF) using Adaptive Neuro-Fuzzy Inference System (ANFIS). Research in Concrete. 4 (2), 7-16 .(In Persian)
[9].   Feiznia, S., Khadjeh, M. and Ghauomian, J. (2005). The study of the effect of physical, chemical and climate factors on surface erosion sediment yield of loess soils (Case study in Golestan province). Journal of Pajouhesh & Sazandegi, 66, 14-24. (In Persian)
[10].    Flanagan, D.C., Ascough, J.C., Geter, W.F., and David. O. (2005). Development of a hillslope erosion module for the object modeling system. ASAE Annual International Meeting. Tampa, Florida, 1-12.
[11].    Ghafari, G., Ahmadi H., Bahmani, O. and Nazari Samani A. (2013). Zoning Sensitive Areas to Erosion in the Kan Basin by Using Geographic Information System (GIS). Geographical Research, 3, 28 (111), 153-166. (In Persian)
[12].    Govers, G. Giménez, R. and Oost, K. V. (2007). Rill erosion: Exploring the relationship between experiments, modelling and field observations. Earth-Science Reviews, 84 (3–4), 87–102.
[13].    Govers, G., Poesen, J. (1998). Assessment of the interrill and rill contributions to total soil loss from an upland field plot. Geomorphology, 1(4), 343–354.
[14].    Hairsine, P. and Rose, C. (1992). Modeling water erosion due to overland flow using physical principles: 2. Rill flow. Water Resources Research, 28 (1), 245–250.
[15].    Horn, R., Fleige, H., Richter, F.H., Czyz, E.A., Dexter, A., Diaz-Pereira, E., Dumitru, E., Enarche, R., Mayol, F., Rajkai, K., De la Rosa, D. and Simota, C. (2005). SIDASS project, Part 5: Prediction of mechanical strength of arable soils and its effects on physical properties at various map scales. Soil Till. Res, 82, 47-56.
[16].    Hoseini, S. and Gorbani, M. (2005). Economics of Soil Erosion, Ferdowsi University of Mashhad Press, Mashhad. (in Persian)
[17].    Hosseini, S.M., Mosaedi, A., Naseri, K. and Golkarian, A. (2012). Modeling the effect of hill slope length on features of rill erosion based on incomplete GAMAMA function in Ahamd-Abad, Mashhhad. Journal of Water and Soil, 26 (5), 1215-1225. (In Persian)
[18].    Hosseini, S.M., Mosaedi. A., Naseri. K. and Golkarian. A. (2012). Identification of the most effective Elements on rill erosion in the hill slope units of Mashhad south west, Iran. Geography and Envioronmental  Hazards, 2, 87-99
[19].    Jang, J. S.R. (1993). Anfis: adaptive-network-based fuzzy inference systems. Journal of IEEE Transactions on System Management and Cybernetics, 23, 665–685.
[20].    Khalilmoghadam, B., Afyuni, M., Abbaspour, K.C., Jalalian, A., Dehghani, A.A. and Schulin, R. (2009). Estimation of surface shear strength in Zagros region of Iran-A comparison of artificial neural networks and multiple-linear regression models. Geoderma, 153, 29-36.
[21].    Khazai, M., Shafie, A. and Molai, A. (2012). Investigation the factors influencing the development of eroding gully in Maroon watershed. Journal of Soil Research (Soil and Water Sciences), 26 (2A), 153-163. (In Persian)
[22].    Kinnell, P.I.A. (2008). Raindrop-impact induced erosion processes and prediction: A review. Hydro Process, 19, 2815-2844.
[23].    Kisi, O., (2005). Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal, 50 (4), 683–696.
[24].    Komasi, M., Taghi Alami, M. and Nourani, V. (2013). Drought forecasting by SPI Index and ANFIS model using Fuzzy C-mean Clustering. Water and Wastewater, 4, 90-102.
[25].    Liu, B.Y, Nearing, M.A. and Rise, L.M. (1994). Slope gradient effects on soil loss for steep slopes. Transactions of the ASAE, 37(6), 1835–1840.
[26].    Liu, Q. Q., Chen, L., Li, J.C. and V.P. Singh. (2007). A non-equilibrium sediment transport model for rill erosion. Hydrological Process, 21, 1074–1084.
[27].    Liu, Q., Li, J., Chen, L. and Xiang, H. (2004). Dynamics of overland flow and soil erosion (II)-soil erosion. Advances in Mechanics, 34 (25), 193–506.
[28].    Mahmudabadi, M., Refahi, H. and Rohi Pour, H. (2013). Evaluating process based WEPP model in prediction of interill erosion by rainfall simulator. Soil Research Journal, 27 (1), 23-34.
[29].    Martín-Rosales, W., Pulido-Bosch, A., Vallejos, A., Gisbert, J., Andreu, J.M. and Sanchez-Martos, F. (2007). Hydrological implications of desertification in southeastern Africa. Hydrological Sciences Journal, 52 (6), 1146-1161.
[30].    Meyer, L.D. and Harmon, W.C. (1984). Susceptibility of agricultural soils to interrill erosion. Soil Soil Science Society of America Journal, 48, 1152-1157.
[31].    Mohamadpour, S., Rouhani, H., Ghorbani Waghei, H. and Seyedian, S. M. (2013). Field experiments for understanding runoff and sediment in rill erosion. First National Conference on Natural Resources. Gonbad, Iran.
[32].    Mohammadi, J. and Taheri, M. (2005). Estimation of pedotransfer function using fuzzy regression. Journal of Agriculture Science and Technology, 2, 51-60. (In Persian)
[33].    Mosaddeghi, M.R., Hajabbasi, M.A. and Khademi, H. (2006). Tensile strength of sand, palygorskite, and calcium carbonate mixtures and interpretation with the effective stress theory. Geoderma, 134, 160-170.
[34].    Nearing, M. A. Foster, G.R., Lan, L.J., Lane, L. J. and Finkner S. C. (1989). A process based soil erosion model for USDA—Water erosion prediction project technology. Transactions of the ASAE, 32(5), 1587–1593.
[35].    Nearing, M.A., Norton, L.D., Bulgakov, D.A., Larionov, L.T., Dontsova, K. M. (1997). Hydraulics and erosion in eroding rills. Water Resource Research, 33 (4), 865–876.
[36].    Nelson, R.E. (1982). Carbonate and gypsum. Methods of Soil Analysis. Agronomy Handbook No 9, American Society of Agronomy and Soil Science Society of America, Madison, Part I (ed.A.L.Page), pp. 181-197.
[37].    Nigel, R. and Rughooputh, S.D.D.V. (2010). Soil erosion risk mapping with new dataset: An improved prioritisation of high erosion risk area, Catena, 82 (3), 191-205.
[38].    Nourani, V. (2014). A Review on Applications of Artificial Intelligence-Based Models to Estimate Suspended Sediment Load. International Journal of Soft Computing and Engineering. 3 (6), 121-127.
[39].    Park, S.W., Mitchell, J.K., Bubenzer, G.D. (1982). Splash erosion modeling: physical analyses. Trans. Am. Soc. Agric. Eng. 25, 357–361
[40].    Poesen, J., Nachtergaele, J., Verstraeten, G. and Valentin, C. (2003). Gully erosion and environmental change: importance and research needs. Catena, 50 (2– 4), 91–133. (In Persian)
[41].    Rauws, G. and Govers, G. (1988). Hydraulic and soil mechanical aspects of till generation on agricultural soils. J. Soil Sci, 39, 111-124.
[42].    Rezaei Banafshe, M., Feyzolahpour, M., Sadrafshary, S. (2013). Using Neural Fuzzy Inference System to Estimate Sediment Load and a Comparison with MLR and SRC Models in Ghranghu River Basin. Physical Geography Research Quarterly, 45 (84), 77-90. (In Persian)
[43].    Salajegheh, A., Fathabadi, A., and Mahdavi, M. (2009). Stream flow Forecasting Using Neuro- Fuzzy and Time Series Methods, Journal of Iran- Watershed   Management   Science   &   Engineering, 2 (5), 21-30. (In Persian)
[44].    Sidorchuk, A. (2009). A third generation erosion model: the combination of probabilistic and deterministic components. Geomorphology, 110 (1–2), 2–10.
[45].    Taghizadeh Mehrjardi, R., Sarmadian, Ph., Savabqy, Gh., Omid, M., Toomanian, n., Rosta, M.J. and Rahimian, M.H. (2013). Comparison of Neuro-Fuzzy, Genetic Algorithm, Artificial Neural Network and Multivariate Regression for Prediction of Soil Salinity (Case study: Ardakan City). Journal of Range and Watershed Management, Natural Resources Journal, 66 (2), 207-222. (In Persian)
[46].    Toy, T. J., Foster, G. R. and Renard, K.G. (2002). Soil erosion processes, prediction, measurement under simulated rainfall. Soil Science, 150, 787-798.
[47].    Ty, P.H. (2008). Soil erosion risk modeling within upland landscapes in Vietnam using remotely sensed data and the RUSLE model. Dalhousie University, Canada, Ph.D. Thesis. 87 p.
[48].    Walkly, A. and Black, I.A. (1934). An examination of digestion methods for determining soil organic matter and a proposed modification of the chromic and titration. Soil Science Society of America Journal, 37, 29-38.
[49].    Warren, I. and Davis, L. (2006). Enhancing pattern classification with relational fuzzy neural networks and square bk-products, PhD thesis, Florida State University, USA.
[50].    Wirtz, S., Seeger, M. and Ries, J.B. (2012). Field experiments for understanding and quantification of rill erosion processes. Catena, 91, 21-34.
[51].    Wirtz, S., Seeger, M., Zell, A., Wagner, C., Wagner, J.F. and Ries, J. (2013). Applicability of different hydraulic parameters to describe soil detachment in eroding rills. Plos One, 8 (5), e64861.
[52].    Wischmeier, W. H. and Smith, D.D. (1978). Predicting rainfall erosion losses. Agricultural Handbook 537. United States Department of Agriculture. Washington. DC. USA.
[53].    Yan, F. L., Shi, Z.H, and Li, Z.X. (2008). Estimating interrill soil erosion from aggregate stability of Ultisols in subtropical China. Soil & Tillage Research, 100 (1–2), 34–41.
[54].    Zarynkafsh, M. (1994). Applied Soil Science, Tehran University Press, 236 pages. (In Persian)
[55].    Zhang, L. and Liu, Y. (2004). An analysis on man-land relationship of eastern China. Acta Geographica Sinica, 59, 311–319.
[56].    Zorratipur, A., Salajegheh A., Ahmadi, H. and Arab Khedri M. )2013(. The Assessment of experimental affect rainfall characteristic’s and hill slope on shear stress and rill formation on marl formation (Case Study: Taleghan Basin). Iran-Watershed Management Science & Engineering, 7 (21): 21-28. (In Persian)