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ارائه مدلی مبنی بر ترکیب روش آماری عامل اطمینان و روش بگینگ به‌منظور اکتشاف آب زیرزمینی | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 16، دوره 50، شماره 10، اسفند 1398، صفحه 2595-2608 اصل مقاله (2.12 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2019.282441.668220 | ||
| نویسندگان | ||
| سید وحید رضوی ترمه1؛ مجید رحیم زادگان* 2 | ||
| 1دانشجو دکتری سامانه اطلاعات جغرافیایی (GIS)، دانشکده ژئودزی و ژئوماتیک، دانشگاه صنعتی خواجه‌نصیرالدین طوسی | ||
| 2ایران، تهران، دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکده عمران، گروه منابع آب | ||
| چکیده | ||
| با توجه به تغییرات اقلیمی و رشد جوامع شهری، نیاز به آب زیرزمینی و اکتشاف این منابع رو به افزایش است؛ بنابراین هدف از تحقیق حاضر، تهیه نقشه پتانسیل سطح آب زیرزمینی با استفاده از سیستم اطلاعات مکانی (GIS) در منطقه‌ای واقع در دشت بوشهر با استفاده از ترکیب روش آماری عامل اطمینان با روش داده‌کاوی بگینگ است. بدین منظور در گام اول، 339 موقعیت چاه در منطقه موردمطالعه مشخص گردید و به‌صورت تصادفی، 238 چاه (70 درصد) به‌عنوان نقاط آموزشی و 101 چاه (30 درصد) به‌عنوان نقاط اعتبارسنجی تعیین گردید. در گام بعد، 15 عامل تأثیرگذار بر تجمع آب زیرزمینی مانند ارتفاع، زاویه شیب، جهت شیب، طول شیب، انحنای سطح، انحنای آبراهه، شاخص رطوبت توپوگرافی، فاصله از گسل، تراکم گسل، فاصله از رودخانه، تراکم آبراهه، بارندگی، لیتولوژی، پوشش اراضی و نوع خاک در نرم‌افزار ArcGIS 10.3 و Saga GIS تهیه گردید. رابطه مکانی بین پارامترهای مؤثر و موقعیت چاه‌ها با استفاده از مدل عامل اطمینان مشخص گردید و به‌منظور پیاده‌سازی مدل بگینگ از این وزن‌ها استفاده شد. به‌منظور ارزیابی دقت مدل ترکیبی از شاخص‌های ضریب تعیین، RMSE و MAE استفاده شد و همچنین به‌منظور ارزیابی دقت نقشه‌های تهیه‌شده از منحنی تشخیص عملکرد نسبی (ROC) و سطح زیر آن (AUC) استفاده گردید. نتایج حاصل از ارزیابی نشان می‌دهد که مقادیر شاخص‌های ضریب تعیین، RMSE و MAE برای داده‌های آموزشی و اعتبارسنجی به ترتیب برابر 76 درصد، 247/0، 162/0، 5/73 درصد، 256/0 و 169/0 است. نتایج ارزیابی منحنی ROC نشان می‌دهد که سطح زیر منحنی به ترتیب 2/86 و 8/94 درصد برای مدل‌های عامل اطمینان و ترکیب مدل عامل اطمینان با مدل داده‌کاوی بگینگ است. | ||
| کلیدواژه‌ها | ||
| پتانسیل سطح آب زیرزمینی؛ مدل عامل اطمینان؛ مدل بگینگ؛ سیستم اطلاعات مکانی (GIS) | ||
| عنوان مقاله [English] | ||
| Introducing a Hybrid Model Based on Certainty Factor Statistical and Bagging Methods for Discovering Groundwater | ||
| نویسندگان [English] | ||
| seyed vahid razavi termeh1؛ Majid Rahimzadegan2 | ||
| 1PhD Student of GIS, Department of Geodesy & Geomatics, Khajeh Nasir Toosi University of Technology | ||
| 2Department of Water resources, Faculty of Civil engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| Due to climate change and growth of urban communities, the need for groundwater and exploration of these resources are increasing. Therefore, the purpose of this study was to provide a groundwater potential mapping using the geographic information system (GIS) in a region located in Booshehr plain using an ensemble of certainty factor (CF) method with Bagging data mining method. For this purpose, in the first step, 339 well locations were identified in the study area, of which 238 wells (70%) were randomly selected as training points and 101 wells (30%) were selected as validation points. In the next step, 15 factors affecting groundwater such as altitude, slope angle, slope direction, slope length, plan curvature, profile curvature, topographic wetness index, distance from fault, fault density, distance from river, drainage density, rainfall, lithology, Soil and land cover were prepared in ArcGIS 10.3 and Saga GIS software. The spatial relationship between the effective parameters and the location of the wells was determined using a CF model. These weights were used to implement the Bagging model. In order to validate the accuracy of the ensemble model, the RMSE and MAE indices were used. Also, in order to validate the accuracy of the maps, ROC and AUC were used. The results of this study showed that the values of RMSE and MAE indices for training and validation are equal to 0.247, 0.162, 0.256 and 0.169 respectively. The evaluation results of the ROC curve indicated that the AUC was 86.2 and 94.8%, respectively, for CF models and the ensemble of CF model with the Bagging model. | ||
| کلیدواژه‌ها [English] | ||
| Groundwater potential mapping, certainty factor (CF) method, bagging method, Geographic Information System (GIS) | ||
| مراجع | ||
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Al-Abadi, A. M., Al-Temmeme, A. A., & Al-Ghanimy, M. A. (2016). A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Sustainable Water Resources Management, 2(3), 265-283. Ayazi, M. H., Pirasteh, S., Arvin, A. K. P., Pradhan, B., Nikouravan, B., & Mansor, S. (2010). Disasters and risk reduction in groundwater: Zagros Mountain Southwest Iran using geoinformatics techniques. Disaster Adv, 3(1), 51-57. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140. Bui, D. T., Ho, T. C., Pradhan, B., Pham, B. T., Nhu, V. H., & Revhaug, I. (2016). GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environmental Earth Sciences, 75(14), 1101. Chen, W., Shahabi, H., Shirzadi, A., Li, T., Guo, C., Hong, H. Ma, M. (2018a). A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto International, 33(12), 1398-1420. Chen, W., Li, H., Hou, E., Wang, S., Wang, G., Panahi, M. Niu, C. (2018b). 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. Clapcott, J., Goodwin, E., & Snelder, T. (2013). Predictive Models of Benthic Macroinvertebrate Metrics. Prepared for Ministry for the Environment. Retrieved from Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., ... & Althuwaynee, O. F. (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. Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology, 41(6), 720-730. Fitts, C. R. (2002). Groundwater science: Elsevier. Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environmental monitoring and assessment, 190(3), 149. Greenbaum, D. (1992). Structural influences on the occurrence of groundwater in SE Zimbabwe. Geological Society, London, Special Publications, 66(1), 77-85. Israil, M., Al-Hadithi, M., & Singhal, D. C. (2006). Application of a resistivity survey and geographical information system (GIS) analysis for hydrogeological zoning of a piedmont area, Himalayan foothill region, India. Hydrogeology Journal, 14(5), 753-759. Jha, M. K., Chowdary, V. M., & Chowdhury, A. (2010). Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeology journal, 18(7), 1713-1728. Jha, M. K., Chowdhury, A., Chowdary, V. M., & 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. Jothibasu, A., & Anbazhagan, S. (2016). Modeling groundwater probability index in Ponnaiyar River basin of South India using analytic hierarchy process. Modeling Earth Systems and Environment, 2(3), 109. Kanungo, D. P., Sarkar, S., & Sharma, S. (2011). Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Natural hazards, 59(3), 1491. Khosravi, K., Panahi, M., & Tien Bui, D. (2018). Spatial Prediction of Groundwater Spring Potential Mapping Based on Adaptive Neuro-Fuzzy Inference System and Metaheuristic Optimization. Kordestani, M. D., Naghibi, S. A., Hashemi, H., Ahmadi, K., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology Journal, 1-14. Lee, S., & Lee, C.-W. (2015). Application of decision-tree model to groundwater productivity-potential mapping. Sustainability, 7(10), 13416-13432. Lee, S., Hong, S.-M., & Jung, H.-S. (2018). GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto International, 33(8), 847-861. Lee, S., Hyun, Y., & Lee, M.-J. (2019). Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea. Sustainability, 11(6), 1678. Lee, S., Kim, Y. S., & Oh, H. J. (2012). Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. Journal of Environmental Management, 96(1), 91-105. Mair, A., & El-Kadi, A. I. (2013). Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. Journal of contaminant hydrology, 153, 1-23. Mogaji, K., Lim, H., & Abdullah, K. (2015). Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model. Arabian Journal of Geosciences, 8(5), 3235-3258. Moore, I. D., Grayson, R., & Ladson, A. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. Moore, I., & Burch, G. (1986). Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resources Research, 22(8), 1350-1360. Mukherjee, S., Aadhar, S., Stone, D., & Mishra, V. (2018). Increase in extreme precipitation events under anthropogenic warming in India. Weather and climate extremes, 20, 45-53. Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9), 2761-2775. Naghibi, S. A., Pourghasemi, H. R., & Abbaspour, K. (2018). A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theoretical and applied climatology, 131(3-4), 967-984. Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S., & 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. Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283-300. Oh, H. J., Kim, Y. S., Choi, J. K., 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. Osati, K., Koeniger, P., Salajegheh, A., Mahdavi, M., Chapi, K., & Malekian, A. (2014). Spatiotemporal patterns of stable isotopes and hydrochemistry in springs and river flow of the upper Karkheh River Basin, Iran. Isotopes in environmental and health studies, 50(2), 169-183. Pham, B. T., Jaafari, A., Prakash, I., Singh, S. K., Quoc, N. K., & Bui, D. T. (2019). Hybrid computational intelligence models for groundwater potential mapping. Catena, 182, 104101. Pourghasemi, H., Moradi, H., & Aghda, S. F. (2013). Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards, 69(1), 749-779. Rahmati, O., Samani, A. N., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2015). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8(9), 7059-7071. Razandi, Y., Pourghasemi, H. R., Neisani, N. S., & 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. Razavi Termeh, S., Mesgari, M., kazemi, K. (2017). Evaluation and comparison of frequency ratio, statistic index and entropy methods for groundwater potential mapping using GIS (Case Study: Jahrom Township). Iranian journal of Ecohydrology, 4(3), 725-736 (in farsi). Tahmassebipoor, N., Rahmati, O., Noormohamadi, F., & Lee, S. (2016). Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arabian Journal of Geosciences, 9(1), 79. Termeh, S. V. R., Kornejady, A., Pourghasemi, H. R., & Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment, 615, 438-451. Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied artificial intelligence, 17(5-6), 375-381.
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