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بررسی دقت روش وزن دهی آنتروپی شانون در تعیین عرصه‌های مناسب تغذیه مصنوعی دشت سرخون | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 4، دوره 47، شماره 2، مرداد 1395، صفحه 247-258 اصل مقاله (762.98 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2016.58331 | ||
| نویسندگان | ||
| محمد کمانگر* 1؛ فیروزه قادری2؛ پیمان کرمی3 | ||
| 1سنندج | ||
| 2دانشگاه علمی کاربردی | ||
| 3کارشناس ارشد، دانشکده منابع طبیعی، دانشگاه هرمزگان | ||
| چکیده | ||
| تغذیه مصنوعی آب‌های زیرزمینی نقش محوری در مدیریت پایدار این منابع دارد. دشت سرخون به دلیل خشک‌سالی و برداشت فزاینده دچار افت سطح ایستابی شده است. هدف از این پژوهش شناسایی عرصه‌های مناسب تغذیه مصنوعی و ارزیابی مدل آنتروپی شانون است. با توجه به مطالعات گذشته و شرایط منطقه، 9 عامل شیب، کیفیت آب، عمق آب، ضریب نفوذپذیری، ضخامت آبرفت، کاربری اراضی، قابلیت انتقال، ریخت‌شناسی و تراکم زهکشی انتخاب و با روش آنتروپی و مقایسه زوجی به ترتیب وزن هر معیار و کلاس‌های هر لایه محاسبه و ترکیب شدند، سپس نقشه نهایی در چهار کلاس پهنه‌بندی شد. نتایج نشان داد مناطق کاملاً مناسب اغلب در واحدهای ریخت‌شناسی واریزه‌های بادبزنی در شمال دشت با شیب‌های کمتر از سه درصد قرار دارند که حدود 7/17 درصد از دشت را به خود اختصاص داده‌اند. ارزیابی نتایج با مقایسه طرح‌های اجرایی موفق در منطقه صورت گرفت که 78 درصد همپوشانی داشت برتری مدل مذکور را می‌توان در نظرگیری تأثیر عدم قطعیت بر وزن هر یک از معیارها دانست که می‌تواند دقت مدل خروجی را بالا ببرد. | ||
| کلیدواژه‌ها | ||
| منابع آب؛ پخش سیلاب؛ تراکم زهکشی؛ درون‌یابی؛ هرمزگان | ||
| عنوان مقاله [English] | ||
| Implementation of Shannon Entropy Method to Determine Areas Suitable for Artificial ground water recharge Case Study: Sarkhoon Plain | ||
| نویسندگان [English] | ||
| mohammad kamangar1؛ Firoozeh Ghaderi2؛ Peymani Karami3 | ||
| چکیده [English] | ||
| Artificial recharge of groundwater plays a pivotal role in the sustainable management of these resources. Sarkhoon plain in Hormozgan was carried out using geographic information system and combining it with the Shannon entropy and a pair-wise comparison test. For this purpose, 9 affecting elements of, water quality, water depth, permeability coefficient, thickness of alluvium, land use, transfer capability, land morphology and drainage density were selected and prepared. Then using entropy method and pair-wise comparisons, the weight of each standard and the classes of each layer were calculated. Next, the areas with constraint for flood spreading are removed and finally the entire area was divided and zoned into four classes using GIS analytical functions and Jencks algorithm. Results showed that drainage density factor weighing 0.211 is the most important factor for locating flood spreading in Sorkhun plain. Areas suited for flood spreading are frequently located at the morphological units of alluvial fan in the north part of the plain, with slopes of less than three percent, and allocated approximately 17.70% of the plain. Evaluate the results by comparing the successful implementation projects in the region was 78 percent overlap model can lead to weight each criterion in considering the impact of the uncertainty, Which can enhance the accuracy of the model output. | ||
| کلیدواژه‌ها [English] | ||
| water resources, Floodwater, drainage density, interpolation, Hormozgan Province | ||
| مراجع | ||
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Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1–4), 28-40. Ale sheikh, A. A., Soltani, M. J., Nouri, N., Khalilzadeh, M., (2008). Land assessment for flood spreading site selection using geospatial information system. International Journal of Environmental Science & Technology, 5(4): 455-462 ASCE STANDARD, (2001). Standard Guidelines for Artificial Recharge of Ground Water, Environmental and Water Resource Institute, American Society of Civil Engineers, ASCE standard, EWRI/ ASCE 106, 34-10.[A1] Chabok Boldaji, M., Hassanzadeh Nofoti, M., Ibrahim Khosfi, Z. (2011). Suitable Areas Selection Using AHP (Case study watershed Ashgabat Tabas), Journal of Science and Engineering watershed, Fourth year, No, 13, 127-14. Chowdhury, A., Chowdhury, Jha, A. M. (2010). Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district, West Bengal, using RS, GIS and MCDM techniques. Environmental Earth Sciences, 59(6): 1209-1222. Faraji Sabokbar, h., et al. [A2] (2012). Identification of suitable areas for artificial groundwater recharge using integrated ANP and pair wise comparison methods in GIS environment, (case study: Garbaygan Plain of Fasa). Geography and Environmental Planning, 44(4): 143-166. (In Farsi) Ghayoumian, J., Ghermez Cheshme, B., Feiznia, S., Noroozi A. (2004). Integrating GIS and DSS for Identification of Suitable Areas for Artificial Recharge (Case study: Meimeh Basin, Isfahan, Iran), journal of science Teacher Training University, 3, 115-131. Gleeson, T., Alley, M., Allen, M., Sophocleous, A., Zhou, Y., Taniguchi, M, & VanderSteen, J., (2012). Towards Sustainable Groundwater Use: Setting Long-Term Goals, Backcasting, and Managing Adaptively, Ground Water, 50(1), 19-26. doi: 10,1111/j,1745-6584,2011,00825,x Khasheii [A3] sivaki, A., Ghahraman, B.Koochek zadeh, M. (2013). Comparison of artificial neural network models, ANFS and regression in the estimation of shallow Neshoba aquifer, Journal of Irrigation and Drainage, 1, 7, 10-22. (In Farsi) Masomi ashkori, H. (2006) Principles of regional planning.Payam. Tehran. 250p. (In Farsi) Magesh, S., Chandrasekar, N. and Soundranayagam, J. (2012). Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques, Geoscience Frontiers, 3(2), 189-196. Mohanty, S., Jha, M, Kumar, A. and Sudheer, K, P. (2010). Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India, Water Resources Management, 24(9), 1845-1865. From: doi: 10,1007/s11269-009-9527-x Pasha, E. and Mostafavi, H. (2013). Calculate the Uncertainty Interval Based on Entropy and Dempster Shafer Theory of Evidence. In: International Journal of Industrial Engineering & Production Management, August 2013, pp. 215-22. (In Farsi) Portaheri, M. (2006) Application of Multi-Attribute Decision Making Methods in Geography. Samt. 232p. (In Farsi) Rahman, M. A., Kasemsan M., and Nuttee, A. (2013). An integrated study of spatial multicriteria analysis and mathematical modelling for managed aquifer recharge site suitability mapping and site ranking at Northern Gaza coastal aquifer. Journal of Environmental Management, 124(0): 25-39. Reddy, k. and Maharaj, V. (2009). World Heritage Site selection in sensitive areas: Andaman and Nicobar Islands. Reconstructing Indian population history, 585p. Sethi, R. R., Kumar, A., Sharma, S. P., & Verma, H. C. (2010). Prediction of water table depth in a hard rock basin by using artificial neural network. International Journal of Water Resources and Environmental Engineering, 2(4), 95-102. http://www.academicjournals.org/journal/IJWREE/article-abstract/F4998981720 Zarcheshme, M., Kheirkhah Zarkesh, M. Davood, Gh. (2011). Combining GIS and Decision Support Systems to Determine Suitable Areas Flood Spreading (study area: Mashkyd watershed in Sistan and Baluchestan province). National Conference of Geomatics. Iran Cartography organization, 9, 87-101. Yazdani Moghadam, Y. (2011). Performance multi-criteria decision method in locating spreading, Case study: Kashan Plain. Journal of Remote Sensing and GIS of Iran, 3:65-80. (In Farsi) Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1–4), 28-40. Ale sheikh, A. A., Soltani, M. J., Nouri, N., Khalilzadeh, M., (2008). Land assessment for flood spreading site selection using geospatial information system. International Journal of Environmental Science & Technology, 5(4): 455-462 ASCE STANDARD, (2001). Standard Guidelines for Artificial Recharge of Ground Water, Environmental and Water Resource Institute, American Society of Civil Engineers, ASCE standard, EWRI/ ASCE 106, 34-10.[A1] Chabok Boldaji, M., Hassanzadeh Nofoti, M., Ibrahim Khosfi, Z. (2011). Suitable Areas Selection Using AHP (Case study watershed Ashgabat Tabas), Journal of Science and Engineering watershed, Fourth year, No, 13, 127-14. Chowdhury, A., Chowdhury, Jha, A. M. (2010). Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district, West Bengal, using RS, GIS and MCDM techniques. Environmental Earth Sciences, 59(6): 1209-1222. Faraji Sabokbar, h., et al. [A2] (2012). Identification of suitable areas for artificial groundwater recharge using integrated ANP and pair wise comparison methods in GIS environment, (case study: Garbaygan Plain of Fasa). Geography and Environmental Planning, 44(4): 143-166. (In Farsi) Ghayoumian, J., Ghermez Cheshme, B., Feiznia, S., Noroozi A. (2004). Integrating GIS and DSS for Identification of Suitable Areas for Artificial Recharge (Case study: Meimeh Basin, Isfahan, Iran), journal of science Teacher Training University, 3, 115-131. Gleeson, T., Alley, M., Allen, M., Sophocleous, A., Zhou, Y., Taniguchi, M, & VanderSteen, J., (2012). Towards Sustainable Groundwater Use: Setting Long-Term Goals, Backcasting, and Managing Adaptively, Ground Water, 50(1), 19-26. doi: 10,1111/j,1745-6584,2011,00825,x Khasheii [A3] sivaki, A., Ghahraman, B.Koochek zadeh, M. (2013). Comparison of artificial neural network models, ANFS and regression in the estimation of shallow Neshoba aquifer, Journal of Irrigation and Drainage, 1, 7, 10-22. (In Farsi) Masomi ashkori, H. (2006) Principles of regional planning.Payam. Tehran. 250p. (In Farsi) Magesh, S., Chandrasekar, N. and Soundranayagam, J. (2012). Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques, Geoscience Frontiers, 3(2), 189-196. Mohanty, S., Jha, M, Kumar, A. and Sudheer, K, P. (2010). Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India, Water Resources Management, 24(9), 1845-1865. From: doi: 10,1007/s11269-009-9527-x Pasha, E. and Mostafavi, H. (2013). Calculate the Uncertainty Interval Based on Entropy and Dempster Shafer Theory of Evidence. In: International Journal of Industrial Engineering & Production Management, August 2013, pp. 215-22. (In Farsi) Portaheri, M. (2006) Application of Multi-Attribute Decision Making Methods in Geography. Samt. 232p. (In Farsi) Rahman, M. A., Kasemsan M., and Nuttee, A. (2013). An integrated study of spatial multicriteria analysis and mathematical modelling for managed aquifer recharge site suitability mapping and site ranking at Northern Gaza coastal aquifer. Journal of Environmental Management, 124(0): 25-39. Reddy, k. and Maharaj, V. (2009). World Heritage Site selection in sensitive areas: Andaman and Nicobar Islands. Reconstructing Indian population history, 585p. Sethi, R. R., Kumar, A., Sharma, S. P., & Verma, H. C. (2010). Prediction of water table depth in a hard rock basin by using artificial neural network. International Journal of Water Resources and Environmental Engineering, 2(4), 95-102. http://www.academicjournals.org/journal/IJWREE/article-abstract/F4998981720 Zarcheshme, M., Kheirkhah Zarkesh, M. Davood, Gh. (2011). Combining GIS and Decision Support Systems to Determine Suitable Areas Flood Spreading (study area: Mashkyd watershed in Sistan and Baluchestan province). National Conference of Geomatics. Iran Cartography organization, 9, 87-101. Yazdani Moghadam, Y. (2011). Performance multi-criteria decision method in locating spreading, Case study: Kashan Plain. Journal of Remote Sensing and GIS of Iran, 3:65-80. (In Farsi)
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