نوع مقاله : مقالات پژوهشی
نویسندگان
1 استادیار خاک‌شناسی، گروه خاک‌شناسی، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ایران
2 استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز، ایران
3 کارشناسی ارشد خاک‌شناسی، گروه خاک‌شناسی، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ایران
چکیده
در اثر فرسایش خاک، مهمترین منبع تولید مایحتاج بشر از بین میرود. به‌همین سبب، لزوم انجام تحقیقات بنیادی و کاربردی در زمینه ارزیابی فرسایش خاک به روش‌های مختلف در راستای مدیریت جامع منابع طبیعی بیش از پیش مورد تاکید قرار گرفته است. هدف از این پژوهش کاربرد عملگرهای منطق فازی در پهنه‌بندی فرسایش خندقی در حوزه آبخیز قلعه گرگ واقع در شهرستان شوشتر می‌باشد. در این پژوهش میزان شش متغیر تأثیرگذار در فرسایش خاک شامل: میانگین وزنی قطر خاکدانه، سدیم قابل جذب، شوری، شن‌ریز، سیلت و رس خاک مطابق روش‌های استاندارد آزمایشگاهی تعیین گردید. سپس کلاس‌بندی مجدد و استانداردسازی لایه‌ها به روش فازی انجام و لایه‌های فازی شده با استفاده از عملگرهای جمع جبری فازی، ضرب جبری فازی و گامای فازی 2/0، 5/0، 8/0 و 9/0 با یکدیگر تلفیق و نتایج حاصله مورد ارزیابی قرار گرفت. در پایان جهت اعتبارسنجی نقشه‌های تهیه شده، از دو روش درصد تطابق نقشه مناطق دارای حساسیت خیلی زیاد و زیاد با نقشه خندق‌های منطقه و شاخص مجموع کیفیت (Qs) استفاده شد. با توجه به نتایج به‌دست آمده و با در نظر گرفتن مساحت پهنه‌های مختلف و میزان درصد خندق‌های رخ داده در هریک از آنها، گامای فازی 9/0 با دارا بودن بیشترین میزان شاخص مجموع کیفیت (73/0) در بین عملگرهای مختلف، همچنین 93/94 درصد تطابق نقشه خندق‌های منطقه با مناطق حساسیت خیلی زیاد و زیاد به‌عنوان بهترین روش جهت تهیه نقشه پهنه‌بندی مناطق حساس به فرسایش خندقی در منطقه مورد مطالعه انتخاب گردید.
کلیدواژه‌ها
عنوان مقاله [English]
Evaluation and Zonation of Sensitive Areas to Gully Erosion Using Fuzzy Logic (Case Study: Ghaleh Gorg Watershed- Shushtar)
نویسندگان [English]
- M.R. Ansari 1
- F. Soleimani 2
- A. Ahmadi 3
1 Assistant Professor Soil Science Department, Agricultural Sciences and Natural Resources University of Khuzestan, Iran
2 Assistant Professor, Soil Conservation and Watershed Management Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, AREEO, Ahvaz, Iran
3 Master of Science in Soil Science, Soil Science Department, Agricultural Sciences and Natural Resources University of Khuzestan, Iran
چکیده [English]
Introduction: Soil erosion is the most widespread form of soil degradation jeopardizing food security worldwide. In Iran, gully erosion is important because about 90% of the country has arid and semi-arid climates and rainfall is not adequately distributed. In such conditions, the absence or lack of vegetation cover with increasing runoff causes more than 2 billion tons of soil losses annually. Therefore, conducting basic and applied researches on soil erosion via different methods for comprehensive management of natural resources is emphasized. The prerequisite for all kinds of erosion, such as gully erosion, is the prediction of the risk of gully formation in different areas susceptible to erosion.
Materials and Methods: The study area is located in Ghaleh Gorg watershed which sub-basin of Shahid Modarres that large part of these farmlands has been destroyed by gully erosion. The purpose of this study was to apply fuzzy logic operators for gully erosion zoning. In this research, six effective parameters on soil erosion including mean weight diameter of aggregate, sodium adsorption ratio, salinity, percent of fine sand, silt and clay were determined according to standard laboratory methods. After re-classification, standardization of prepared layers was carried out by the Fuzzy method. Hence, Fuzzy-based layers were integrated using operators of Fuzzy algebraic sum, Fuzzy algebraic production and Fuzzy gamma with 0.2, 0.5, 0.8, 0.9 values and the obtained results were evaluated. The validation of prepared maps was done based on two methods of map matching percentage of areas with very high and high sensitivity with gullies map of the study area and quality sum index (Qs).
Results and Discussion: The Fuzzy results of raster layers showed the relative accumulation of silt, clay and fine sand grains in the middle to western parts of the region due to leaching and soil aggregation of Aghajari formation with a high slope of >20%. The deposition of this sediments was with 0-5% slope in the middle and western part of the basin. Results of layer integration indicated the fact that the fuzzy summation and multiplication method are not suitable approaches for final mapping because of their high increasing and decreasing effects, respectively. About Fuzzy Gamma operator 0.2, the results revealed that about 17.07% of the area was in the high and very high-risk zone and 67.07% of the area was in the low risk zone. In Fuzzy Gamma 0.5, about 31.16% of the area was in high risk and 55.38% in low risk zone. And only 60.38 percent of the gullies was in the high-risk area. Thus, these both operators 0.2 and 0.5 cannot be an acceptable method for preparing the final fuzzy map. The results of gamma operator 0.8 showed that about 43.21% of the area was in high and very high risk classes and 42.45% of the area was in low and very low risk classes. In the gamma operator 0.9, about 60.92% of the area was in high and very high risk zone and 17.1% of the area was in low and very low-risk zone. Also, regarding the distribution range of gullies, 94.93% of gullies was in high and very high risk classes, which is more acceptable and better than gamma 0.8. According to the obtained results, Fuzzy gamma 0.9 with 94.93 matching percentage of areas containing very high and high sensitivity and maximum quality sum index (0.73) among different operators was selected as the best method for preparing Fuzzy map in the study area.
Conclusion: According to the results of this study and its comparison with field observations, effective factors contributing to the initiation and development of gully erosion were sensitivity of the geological formations, soil texture type, salinity and alkalinity and non-implementation of biological and biomechanical operations to the soil and vegetation cover restoration. Around 3855 hectares (60.38%) out of 6327.5 hectares of the total studied area were at high and very high erosion risk. Furthermore, 2056 hectares (94.93%) out of the 2166 hectares of the gullies area were at high and very high risk of erosion, denoting the high accuracy of the final map. Among the available methods, Fuzzy Gamma 0.9 with the highest overlap between the area of high and very high-risk classes of the gully map (94.93%), and the highest qualitative sum index (0.73), was selected as the best fuzzy method for zoning in the study area.
کلیدواژه‌ها [English]
- Fuzzy layer
- Fuzzy Operator
- GIS
- Gully erosion
- zonation
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1- Ahmadi H., and Mohammadi A.A. 2007. Gully land management by investigating the effective factors and model presentation to determine the linear progress of gullies (Case study: Hablehroud watershed). 4th national conference on watershed management. Iran, Karaj. Tehran University. (In Persian)
2- Armin M., Mahdian M.H., Ahmadi H., Rohipour H., Salajegheh A., and Ghorbannia Kheybari V. 2014. Investigation on spatial variability of aggregate stability and the factors affecting soil aggregation using kriging geostatistics method (Case study: A part of Taleghan watershed). Watershed Management Research 27(3): 107-122. (In Persian)
3- Castillo C., and Gómez J.A. 2016. A century of gully erosion research: Urgency, complexity and study approaches. Review Article. Earth-Science Reviews 160: 300-319.
4- Chaplot V. 2013. Impact of terrain attributes, parent material and soil types on gully erosion. Geomorphology 186: 1-11.
5- Deng Q., Qin F., Zhang B., Wang H., Luo M., Shu C., Liu H., and Liu G. 2015. Characterizing the morphology of gully cross-sections based on PCA: A case of Yuanmou Dry-Hot Valley. Geomorphology 228: 703-713.
6- Dimoyiannis D.G., Tsadilas C.D. and Valmis S. 1998. Factors affecting aggregate instability of Greek agricultural soils. Communications in Soil Science and Plant Analysis 29: 1239–1251.
7- Entezari M., Maleki A., and Moradi KH. 2014. Zoning of gully erosion in catchment of Dyreh by analytical hierarchy process (AHP). The Journal of Spatial Planning 17(4): 63-86. (In Persian)
8- Erfanian M., Ghahramani P., and Saadat H. 2014. Mapping of soil erosion potential hazard using Fuzzy logic in golestan Gharnaveh watershed. Watershed Management Science & Engineering 7(23): 43-52. (In Persian)
9- Esfandiari Darabad F., Beheshti Javid A., and Fathi M.H. 2013. Gully erosion susceptibility evaluation using fuzzy logic model (Case study: Golestan dam catchment – Ghornave river). 2nd International conference on environmental hazards. Iran, Tehran. Kharazmi University. (In Persian)
10- Gabris G., Kertesz A. and Zambo L. 2003. Land use change and gully formation over the last 200 years in hilly catchment. Catena 50: 151- 164.
11- Huang Ivy B., Keisler J., and Linkov I. 2011. Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of the Total Environment 409(19): 3578 -3594.
12- Idowu O.J. 2003. Relationships between aggregate stability and selected soil properties in humid tropical environment. Communications in Soil Science and Plant Analysis 3: 695–708.
13- Jokar Sarhangi I., and Mortezaei N. 2016. Comparison of Frequency Ratio and Confidence Factor Models in badland erosion zoning (Case study: Jajrood watershed). Quantitative Geomorphological Research 6(2): 161-172. (In Persian)
14- Khosravi Y., and Abasi S. 2016. Geostatistical Analyst. Azarkelk Publisher. First Edition. 133p.
15- Liao Z., Xu Z., Wang D., Lu S., and Hannam P.M. 2011. River Environmental Decision Support System Development for Suzhou Greek in Shanghai. Journal of Environmental Management 92(9): 2211-2221.
16- Madadi A., and Hemmati T. 2015. Karstformation zoning capability using Fazzy logic model (Case study: Ragion Namakabroud of Chalous City). Applied Geomorphology of Iran 3(5): 89-102. (In Persian)
17- Marzolff I., and Poesen J. 2009. The potential of 3D gully monitoring with GIS using high-resolution aerial photography and a digital photogrammetry system. Geomorphology 111(1-2):48-60.
18- Mohammad Ebrahimi M., Javadi M.R., and Vafakhah M. 2015. Investigation of the Role of Soil and Non-Soil Parameters in Development of Linear Gully Erosion in Agh Emam Watershed (2). Iranian Journal of Soil Research 29(4): 487-498. (In Persian)
19- Motevalli S., and Esmaeli R. 2013. Landslide Hazard Zoning using Gamma Fuzzy Operator (A Case Study: Taleghan Watershed). Quarterly journal of Environmental Erosion Research 2(4): 1-20. (In Persian)
20- Nowhegar A., Heidarzadeh M. 2011. The study of physical - chemical characteristics and morphometery of gullying area (case study: Gezir, Hormozgan province). Environmental Erosion Research Journal 1(1): 29-44. (In Persian)
21- Poesen J., Nachtergaelea J., Verstraetena G., and Valentin C. 2003. Gully erosion and environmental change: importance and research needs. Catena 50: 91–133
22- Qilin Y., Jiarong G., Yue W., and Bintian Q. 2011. Debris flow characteristics and risk degree assessment in Changyuan gully, Huairou District, Beijing. Procedia Earth and Planetary Science 2: 262–271.
23- Rahi Gh.R., Nazari Samani A.A., and Ghoddousi J. 2011. Validation of the predicting model for land vulnerability to gully erosion in arid watersheds (Case study: Boushehr province). Journal of Range and Watershed Management 64(3): 281-294. (In Persian)
24- Shadfar S. 2015. Application of Fuzzy logic operators to investigate gully erosion using geographic information system (GIS) (Case study: Troud watershed basin). Scientific - Research Quarterly of Geographical Data 23(92): 35-42. (In Persian)
25- Shafiee A., Mollaee A., and Khazaee M. 2011. Gully erosion threaten in Dehdasht watershed. 7th National conference on watershed management, Isfahan, Iran. 7p. (In Persian)
26- Shahab Arkhazloo H., Emami H., Haghnia Gh.H., and Esmali A. 2016. Comparing the Analytic Hierarchy Process and fuzzy logic for zoning the risk of gully erosion in the three regions of the Ardebil province. Quarterly journal of Environmental Erosion Research 6:1(21): 1-16. (In Persian)
27- Shahrivar A., Shadfar S., Khazae M., and Adeli B. 2017. Assessment of Gully Erosion Zonation Methods (Case study: Abgendi Watershed). Iranian Journal of Eco Hydrology 4(1): 119-132. (In Persian)
28- Sobhe Zahedi Sh., Shadfar S., Alidoost M., pournasrolla M.R., Ghodrati A., and Dadashi A.A. 2012. Calibration, validation, and completion of predictive model of soil sensitivity to gully erosion in arid and semi-arid watersheds(Guilan Province). Final report of project. Soil conservation and watershed management research institute 70p. (In Persian)
29- Soleimani F., Soufi M., and Arsham A. 2017. Determination of effective factors in gullies development in Modares watershed of Shushtar. Journal of Water and Soil 31(5): 1432-1446. (In Persian with English abstract)
30- Taskin Gumus A. 2009. Evaluation of hazardous waste transportation firms by using a two step fuzzy –AHP and TOPSIS methodology. Expert Systems with Applications 36(2): 4067-4074.
31- Valentin C. J., Poesen J., and Yong Li. 2005. Gully erosion: Impacts, factors and control. Catena 63: 132–153.
32- Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena 72: 1-12
33- Yamani M., Zamanzadeh S.M., and Ahmadi M. 2013. Influence of physical and chemical properties of soil on formation and development of gully erosion (Case Study: Kahoor Plain in Fars Province). Quantitative Geomorphological Research 2(2): 135-156. (In Persian)
34- Zadeh L.A. Fuzzy sets. Information and control. June 1965 8(3): 338-353.
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