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مدلسازی و ارزیابی پتانسیل چشمههای آب زیرزمینی مبتنی بر GIS در حوزه آبخیز عمرک | ||
| تحقیقات آب و خاک ایران | ||
| دوره 52، شماره 8، آبان 1400، صفحه 2035-2047 اصل مقاله (2.27 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2021.320753.668919 | ||
| نویسندگان | ||
| حامد کریمی1؛ فریبرز یوسفوند* 2؛ سعید شعبانلو3؛ احمد رجبی4 | ||
| 1دانشجوی دکتری، گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران | ||
| 2گروه مهندسی آب، واحد کرمانشاه، دانشگاه ازاد اسلامی، کرمانشاه | ||
| 3دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران | ||
| 4گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران | ||
| چکیده | ||
| امروزه تأمین آب به منظور تحقق اهداف توسعۀ پایدار، یکی از مهم ترین چالش ها در اکثر کشورهای جهان است. به همین دلیل، تعیین مناطق دارای پتانسیل آب زیرزمینی، از ابزارهای مهم در حفاظت، مدیریت و بهره برداری از منابع آب به شمار می رود. بنابراین هدف از این پژوهش تهیه نقشه پتانسیل چشمههای آب زیرزمینی با استفاده از یک مدل مشهور یادگیری ماشین (جنگل تصادفی) و یک مدل آماری (مدل نسبت فراوانی) و مقایسه کارایی این روشها در حوزه آبخیز عمرک، استان تهران میباشد. ابتدا 18 عامل مؤثر در ظهور چشمهها شامل: سازندهای سنگشناسی، فاصله از گسل، تراکم گسل، طبقات ارتفاعی، درصد شیب، جهت شیب، فاکتور طول شیب، نقشههای انحنا، فاصله از آبراهه، تراکم آبراهه، حداکثر ارتفاع، شاخص نمناکی، موقعیت شیب نسبی، بافت خاک، شاخص زبری سطح، شاخص همگرایی جریان و پوشش کاربری اراضی انتخاب شدند و نقشه آن‌ها در نرمافزار ArcGIS10.5 و [1]SAGA تهیه گردید. پس از بررسی هم‌خطی و طبقه‌بندی لایههای مؤثر با استفاده از روش شکستن طبیعی[2]، درصد فراوانی پتانسیل آب زیرزمینی در هر طبقه با استفاده از هم‌پوشانی نقشه پراکنش چشمهها با هرکدام از لایه‌ها به دست آمد. برای ارزیابی عملکرد مدلهای مذکور از منحنی مشخصه عملکرد نسبی(ROC) استفاده شد و میزان مساحت زیر منحنی (AUC) مدلهای جنگل تصادفی و نسبت فراوانی به ترتیب 88 و 72درصد بهدست آمد. نتایج نشان داد که هر دو روش تخمینگرهای مناسبی برای تهیه نقشه پتانسیل چشمه آب زیرزمینی در منطقه مورد مطالعه هستند. اما مدل جنگل تصادفی با مقدار مساحت زیر منحنی بالاتر روش بهتری برای شناسایی و پهنهبندی پتانسیل چشمههای آب زیرزمینی معرفی گردید. [1] System for automated geoscientific analyses [2] Natural Break | ||
| کلیدواژه‌ها | ||
| پتانسیل آب زیرزمینی؛ چشمه؛ مدل نسبت فراوانی؛ مدل جنگل تصادفی؛ منحنی مشخصه عملکرد نسبی | ||
| عنوان مقاله [English] | ||
| GIS-based Groundwater Spring Potential Modelling and Assessment Mapping in the the Omarak Watershed | ||
| نویسندگان [English] | ||
| hamed karimi1؛ fariborz yosefvand2؛ saeid shabanlou3؛ ahmad rajabi4 | ||
| 1Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| 2water Dept., kermanshah branch, islamic azad university, kermanshah, iran | ||
| 3Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| 4Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| چکیده [English] | ||
| Nowadays, in most countires, water supply in order to achieve the objectives of sustainable development is one of the most important challenges. Because of this, one of the important tools in the protection, management and exploitation of water resources, is to determin groundwater areas. Therefore, the purpose of this study, is to prepare the potential map of groundwater springs, using a well-known machine-learning model (i.e. random forest) and a statistical model (i.e. frequency ratio model) and comparing the efficiency of these methods in the Omarak watershed, Tehran Province. First, 18 factors influencing the emergence of springs including: lithological formations, the distance from the fault, fault density, elevation classes, slope percentage, slope direction, slope length factor, curvature maps, distance from the stream, stream density, maximum height, wetness index, relative slope position, soil texture, terrain roughness index, flow convergence index and land use cover were selected and their maps were prepared in the ArcGIS10.5 and SAGA systems. After the Multicollinearity test and classification of the effective layers, using the natural fracture method, then, the percentage of groundwater potential frequency in each layer obtained using the overlap of the distribution map of the springs with each of the layers. The relative operating characteristic (ROC) curve was used to evaluate the performance of the mentioned models and the area under the curve (AUC) of the random forest models and the frequency ratio were 88 and 72%, respectively. The results indicated that both methods are suitable to estimators to prepare the groundwater source potential map in the studied area. However, the random forest model with a higher area under the curve was introduced as a better method to identify and zoning the potential of groundwater springs. | ||
| کلیدواژه‌ها [English] | ||
| Groundwater potential, spring, frequency ratio model, stochastic forest model, relative operating characteristic curve | ||
| مراجع | ||
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Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., & Ahmad, B.B. (2020). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment, 701, 134-979. Daoud, J.I. (2017). Multicollinearity and regression analysis. J. Phy, Conference Series, 949(1), pp. 012009). IOP Publishing. Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., and Ryu, I.C. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135–165. Felicĺsimo, Á., Cuartero, A., Remondo, J., and Quirόs, E. (2013). Mapping landslide susceptibility with logistiv regression, multiple adaptive regression splines, classification and regression tress, amd maximum entropy methods: a comparative study. Landslides, 10, 175-189. Erfanian, M., Farajollahi, H., Souri, M., and Shirzadi, A. (2016). Comparing the Efficiency of Weight of Evidence, Logistic Regression and Frequency Ratio Methods for Mapping Groundwater Spring Potential in Ghelgazi Watershed, Kordestan Province of Iran . Journal of Water and Soil Science, 20 (75), 59-72. (In Farsi) Ghosh, S., and Carranza, E.J.M. (2010). Spatial analysis of mutual fault/fracture and slope controls on rock sliding in Darjeeling Himalaya, India. Geomorphology 122, 1- 24. Greene, W.H. (2000). Econometric Analysis (4th Edition),Upper Saddle River,NJ: Prentice Hall. Jha, M. K., Chowdhury, A., Chowdary, V. M., and Peiffer, S. (2007). Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resources Management, 21(2), 427-467. Jafarian, Z., Kargar, M., and Bahreini, Z. (2019). Which spatial distribution model best predicts the occurrence of dominant species in semi-arid rangeland of northern Iran. Ecological informatics, 50, 33-42. Kouhi Kamali, M., and Rajabi, M. (2010). Determining the impact of urban green space on the value of artificial use. Journal of Application of GIS and RS in Planning, 1(1), 31-23. (in Farsi). Lee, S., and Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4, 33-41. Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Soleiman, W.N.A., and Ramli, M.F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences. 7, 711–724. Moghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., and Pradhan, B. (2015). Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences, 8(2), 913-929. Naghibi, S.A., Pourghasemi, H.R., Pourtaghi, Z.S., and Rezaei, A. (2015). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1), 171-186. Nguyen, P.T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., Van Phong, T., Sharma, R., Kumar, R., Le, H.V., Ho, L.S., Prakash, I., Pham, B.T. (2020). Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences, 10(7), 2469. Oh, H.J., Kim, Y.S., Choi, J.K., Park, E., and Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4), 158-172. Ozdemir, A. (2011). GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey)using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411, 290-308. Pourtaghi, Z.S., and Pourghasemi, H.R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22(3), 643-662. Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques, Central European Journal of Geosciences, 1(1), 120–129. Rangzan, K., Charchi, E., and Amiri, F. (2005). Comparison of the effect of structural-georphological factors on the emergence of springs in the area of Qala-e-Razeh-Bidrobeh with the Pabdeh anticline in the northwest and northeast of Khuzestan. Proceedings of the Ninth Conference of the Geological Society of Iran, Tehran Teacher Training University, 539-526. (in Farsi). Seyed ali, S., Rahimi, M., Dastourani, j., and Khosroshahi, M.(2016). Trend Analysis of Hydroclimatological Parameters and Detection of Manageral Changes in Water Resources Conditions of Hablerood Watershed. Iranian Journal of Range and Desert Research, 23, 555-556 (in Farsi). Servati, M.R., Ghahrodi, T.M., Golkarami, A., and Njafi, E. (2014). Geomorphological thresholds for gully erosion in Kchick watershed, NE Golestan Province. Applied researches in geographical sciences, 32, 231-249. (in Farsi). Shataee, S., Weinaker, H., and Babanejad, M. (2011). Plot- level Forest Volume Estimation Using Airborne Laser Scanner and TM Data, Comparison of Boosting and Random Forest Regression Algorithms. Procedia Environmental Sciences, 7: 68-73. Shirzadi, A., Saro, L., Hyun-Joo, O.H., and Chapi, K. (2012). A GIS-based logistic regression model in rock fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazard. 64, 1639-1656. Zabihi, M., Pourghasemi, H., and Behzadfar, M. (2015). Groundwater Potential Mapping using Shannon's Entropy and Random Forest Models in the Bojnourd Township. Iranian journal of Ecohydrology, 2(2), 221-232. (In Farsi) Zandi, S., Soleimani, K., and Zandi, J. (2016). Mapping Groundwater Spring Occurrence Potential using Logistic Regression Statistical Method (Case Study: Miredeh Mountainous Watershed, Kurdistan). Journal of Watershed Management Research, 6 (12), 75-87. (In Farsi) Zare Chahouki, M., Khalsi Ahvazi, L., and Azarnivand, H. (2014). Plant Species Distribution Modeling Using Logistic Regression Models in the North East of Semnan. Journal of Range and Watershed Management (Iranian Journal of Natural Resources), 67(1), 45-59. (In Farsi)
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