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شبیهسازی مکانی ظرفیت اندوختههای آب زیرزمینی آبخیز تلوار با مدلهای ماشین بردار پشتیبان و جنگل تصادفی | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 35، شماره 1 - شماره پیاپی 134، فروردین 1401، صفحه 73-87 اصل مقاله (1.79 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.354418.1407 | ||
| نویسندگان | ||
| ادریس سیفی سلسله1؛ محمدصدیق قربانی* 2؛ نگین َعالی2 | ||
| 1دانشجوی کارشناسی ارشد ژئومورفولوژی، گروه جغرافیا، دانشگاه پیام نور، ایران | ||
| 2استادیار ژئومورفولوژی، گروه جغرافیا، دانشگاه پیام نور، ایران | ||
| چکیده | ||
| در این پژوهش ظرفیت اندوختههای آب زیرزمینی بخشهای گوناگون آبخیز تلوار با دو مدل یادگیری ماشینی بردار پشتیبان و جنگل تصادفی شناسایی شد. اطلاعات چاههای منطقه از شرکت آب منطقهیی کردستان گرفته شد. چاههای موجود در منطقه بهشیوهی تصادفی به دو گروه آموزش (70% از دادهها) و اعتبارسنجی (30% از دادهها) تقسیم شد. عاملهای ارتفاع، شیب زمین، جهت شیب، سنگشناسی، خاکشناسی، انحنای سطح، کاربری زمین، شاخص رطوبت پستیوبلندی و فاصله از رود متغیرهای پیشبینیکننده انتخاب، و نقشهی آنها در سامانهی اطلاعات جغرافیایی تهیه شد. دادههای گروه آموزش به همراه نقشههای متغیرهای پیشبینیکننده به مدل ماشین بردار پشتیبان و مدل جنگل تصادفی وارد کردهشد. متغیرهای مدل برپایهی دادههای گروه آموزش تنظیم شد، و برپایهی آن توان اندوختههای آب زیرزمینی پیشبینی شد. دقت پیشبینی مدلها با روش آماری منحنی مشخصهی عملکرد در دو مرحلهی آموزش و اعتبارسنجی تعیین شد. نتیجهها نشان داد که دقت مدل جنگل تصادفی (98/4%) بیشتر از ماشین بردار پشتیبان (98/1%) است. | ||
| کلیدواژهها | ||
| آبخیز تلوار؛ آب زیرزمینی؛ سامانهی اطلاعات جغرافیایی؛ شبیهسازی | ||
| عنوان مقاله [English] | ||
| Spatial Modeling of Groundwater Resources Potential in Telvar Watershed using Support Vector Machine and Random Forest Models | ||
| نویسندگان [English] | ||
| Edris Sayfi Selseleh1؛ Mohammad Sedigh Ghorbani2؛ Negin Aali2 | ||
| 1M.Sc. Student, Department of Geography, Payame Noor University, Iran | ||
| 2Assistant Professor, Department of Geography, Payame Noor University, Iran | ||
| چکیده [English] | ||
| In this research, it is tried to identify the potential status of groundwater resources in different parts of the Telvar watershed using two machine learning models including support vector machine and random forest models. Initially, information about the wells in the region was received from the Regional Water Company of Kurdistan. The wells in the area were randomly divided into two groups of training (including 70% of data) and validation (including 30% of data). Elevation, slope, slope direction, lithology, pedology, surface curvature, land use, topographic moisture index and distance from the river were selected as predictor variables and their map was prepared in the GIS environment. The data of the training group along with the maps related to the predictor variables were entered into the support vector machine model and the random forest model. Based on the data of the training group, the parameters of the model were calibrated and adjusted and the potential of groundwater resources was predicted. The prediction accuracy of the models was determined using the statistical method of performance characteristic curve in two stages of training and validation. The results showed that accuracy of the random forest model (98.4%) was more than the support vector machine model (98.1%). | ||
| کلیدواژهها [English] | ||
| GIS, groundwater, modeling, Telvar watershed | ||
| مراجع | ||
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Abasi S, Heydari M. 2017. The groudwater potential assessment by the use of AHP and Fuzzy technuques (Case study of Northern Basins of Ilam Province). Hydro Geomorphology. 3(6):75–93. (In Persian). Adiat KAN, Nawawi MNM, Abdullah K. 2012. Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool–a case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology. 440–441:75–89. Alizadeh A. 2003. Principles of applied hydrology, Sixteenth edition. Imam Reza University Publication. Mashhad. (In Persian). Andualem TG, Demeke GG. 2019. Groundwater potential assessment using GIS and remote sensing: A case study of Guna Tana landscape, upper blue Nile Basin, Ethiopia. Journal of Hydrolgy: Regional Studies. 24(100610): 1–13. Ansari MS, Shamsaie A, Bwani AM. 2011. Modeling of groundwater level changes in Sefid Dasht by GMS model. The 2nd Iranian National Conference on Applied Research in Ware Resources. Zanjan. 9 p. (In Persian). Arabameri A, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J. 2019. GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci. Total Environ. 646–697(658): 160–177. Ataei H, Ghaderi N, Ghaderzadeh H. 2012. Investigating the relationship of precipitation, humidity, evaporation and groundwater level fluctuations of Dehgolan aquifer. Geographical Research. 26(4):187–208. (In Persian). Breiman L. 2001. Random forests. Machine Learning. 45(1): 5–32. Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, Xiao L. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of the Total Environment, 634: 853–867. Choubin B, Rahmati O. 2021. Groundwater potential mapping using hybridization of simulated annealing and random forest. Editor(s): Pijush Samui. Hossein Bonakdari. Ravinesh Deo. Water Engineering Modeling and Mathematic Tools. Elsevier. 391–403. Choubin B, Rahmati O, Soleimani F, Alilou H, Moradi E, Alamdari N. 2019. Regional groundwater potential analysis using classification and regression trees. Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier. 485–498. Cortes C, Vapnik V. 1995. Support-vector networks. Machine Learning. 20(3): 273–297. Ehteshami M, Aghasi A, Rezaie RA. 2002. Investigation of the evolution of groundwater potential in Hashtgerd Plain in the last ten years and its causes. Journal of Environmental Science and Technology (JEST). 13(4): 61–74. (In Persian). Falah F, Ghorbani Nejad S, Rahmati O, Daneshfar M, Zeinivand H. 2017. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto International. 32(10):1069–1089. Fawcett T. 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27(8):861–874. Friedl MA, Brodley CE, Strahler AH. 1999. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geoscience Remote Sense. 37(2): 969–77. Ghafori S. 2008. Development and management of water resources in Iran to deal with droughts, 3rd Iran Water Resources Management Conference, Tabriz. 746 p. (In Persian). Golkarian A, Naghibi SA, Kalantar B, Pradhan B. 2018. Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess. 190 (3):149. Hesami A. 2016. Study of rainfall trend and the volume of groundwater in Marivan Plain using geographic information systems. Environment and Water Engineering. 2(1): 13–24. (In Persian). Jaafarzadeh MS, Tahmasebipour N, Haghizadeh A, Pourghasemi H, Rouhani H. 2021. Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Scientifc Reports. 11(1): 1–18. Jensen JR. 2015. Introductory digital Image pProcessing: A remote sensing perspective, 4th edn. Prentice Hall Press, Englewood Cliffs. 544 p. Khashei SA, Ghahrman B, Kouchakzadeh M. 2011. Fuzzy-analytic hierarchy process method for evaluating groundwater potentials of aquifers (Case study: Nayshabur Plain). Iranian Water Researches Journal. 5(9): 171–180. (In Persian). Khoshhal J, Ghayoor A, Moradi M. 2012. A survey on the impact of groundwater drought in Dehgolan Basin, Kurdistan Province. Physical Geography Research Quarterly. 44(1): 19–36. (In Persian). Kordestani MD, Naghibi SA, Hashemi H, Ahmadi K, Kalantar B, Pradhan B. 2019. Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeol Journal. 27(1): 211–224. Machiwal D, Jha MK, Mal BC. 2011. Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resources Management. 25(5):1359–1386. Miraki S, Zanganeh SH, Chapi K, Singh VP, Shirzadi A, Shahabi H, Pham BT. 2019. Mapping groundwater potential using a novel hybrid intelligence approach. Water Resources Management. 33(1):281–302. Mosavi A, Hosseini FS, Choubin B, Goodarzi M, Dineva AA, Sardooi ER. 2021. Ensemble boosting and bagging based machine learning models for groundwater potential prediction. Water Resources Management. 35(1): 23–37. Mountrakis Giorgos. Im Jungho. Ogole Caesar. 2011. Support vector machines in remote sensing: A review, ISPRS Journal of Photogrammetry and Remote Sensing. 66(3): 247–259. Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O. 2017. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. Journal of Hydrology. 544–555(548): 471–483. Oh HJ, Kim YS, Choi JK, Park E, 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. Pal M. 2005. Random Forest classifier for remote sensing classification. International Journal of Remote Sensing. 26(1): 217–22. Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S. 2020. Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology. 580–591(588): 1–12. Patra S, Sahoo S, Mishra P, Mahapatra SC. 2018. Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. Journal of Urban Management. 7(2):70–84. Pham BT, Jaafari A, Prakash I, Singh SK, Quoc NK, Bui DT. 2019. Hybrid computational intelligence models for groundwater potential mapping. Catena. 172–183(182): 104101. Rahimi D. 2012. Potential ground water resources: (Case study: Shahrekord Plain). Geography and Environmental Planning. 22(4):127–142. (In Persian). Rahimi M, Solaimani K. 2017. Remote sensing and GIS based assessment groundwater potential zones mapping using multi-criteria decision making technique. Iranian Journal of Watershed Management Science. 10(35): 27–38. (In Persian). Rahmati O, Pourghasemi HR, Melesse AM. 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. Catena. 136–147(137):360–372. Ramezani Charmahineh A, Zounemat Kermani M. 2015. Evaluation of support vector machine, and linear and quadratic discriminant analysis for groundwater level variations in Shahrekord Plain. Iran-Water Resources Research. 11(2):97–105. (In Persian). Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O. 2015. Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Science Informatics. 8(4):867–883. Rezaei Moghaddam MH, Rahimpour T, Nakhostinrouhi M. 2016. Potential detection of the groundwater resources using analytic network process in geographic information system (Case study: Basins Leading to Tabriz Plain). Ecohydrology. 3(3):379–389. (In Persian). Yesilnacar E, Topal T. 2005. Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey). Eng. Geol. 79(3–4): 251–266. Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M. 2016. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences. 75(8):1–19. | ||
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