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پتانسیل یابی چشمه‌های آب معدنی با استفاده از مدل های آماری (مطالعۀ موردی حوضۀ وازرود مازندران) | ||
| اکوهیدرولوژی | ||
| مقاله 20، دوره 8، شماره 3، مهر 1400، صفحه 867-889 اصل مقاله (2.35 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2021.328803.1537 | ||
| نویسندگان | ||
| کریم سلیمانی* 1؛ علیرضا متولی2 | ||
| 1استاد گروه مهندسی آبخیزداری، دانشگاه کشاورزی و منابع طبیعی ساری | ||
| 2دانش‌آموختۀ دکتر‌ی مهندسی آبخیزداری، دانشگاه تربیت مدرس | ||
| چکیده | ||
| تعیین پتانسیل چشمۀ آب‌معدنی از موارد مهم و ضروری در مدیریت منابع آب در مناطق معتدل ایران محسوب می‌شود. در این پژوهش، نقشۀ پتانسیل چشمه به منظور مدیریت و برنامه‌ریزی منابع آبی زیرزمینی با استفاده از مدل‌های آماری نسبت تراکم (FR) و وزن واقعه (WOE) در حوضۀ آبخیز وازرود استان مازندران انجام شد. به این منظور، 57 چشمه در فاز واسنجی [34] و صحت‌سنجی [23] به‌صورت تصادفی مشخص شدند. در اجرای هر دو مدل از عوامل مؤثر بر پتانسیل چشمه شامل درصد شیب، جهت شیب، هیپسومتری، پلان و پروفیل دامنه، زمین‌شناسی، کاربری اراضی، شاخص رطوبت توپوگرافی، فاصله از گسل، تراکم گسل، تراکم آبراهه و فاصله از آبراهه و فاصله از جاده استفاده شد. همچنین، برای صحت‌سنجی مدل‌ها از منحنی ROC استفاده شد. نتایج نشان داد صحت مدل نسبت فراوانی 4/84 درصد و صحت مدل وزن واقعه 2/77 درصد تعیین شد. این نتایج بیانگر صحت خیلی خوب و خوب این دو مدل در تعیین مناطق مستعد چشمه در حوضۀ آبخیز وازرود است. مطابق نتایج، صحت مدل نسبت فراوانی نیز بیشتر از مدل وزن واقعه است. با توجه به نتایج، 7/39 و 9/34 درصد از آبخیز وازرود در طبقات زیاد و خیلی زیاد به لحاظ پتانسیل چشمه به‌ترتیب در دو مدل وزن واقعه و نسبت فراوانی قرار دارد. در نهایت، نقشه‌های پتانسیل چشمه می‌تواند برای تهیۀ زیرساخت‌های مقدماتی به منظور پاکسازی چشمه‌ها از آسیب‌های انسانی و ورود سرمایه‌گذار برای احداث کارخانۀ چشمه و در نهایت، رونق اقتصادی محلی آبخیزنشینان حوضۀ وازرود به کار آید. | ||
| کلیدواژه‌ها | ||
| آب آشامیدنی؛ پتانسیل چشمه؛ مدیریت منابع آب؛ نسبت تراکم؛ وزن واقعه | ||
| عنوان مقاله [English] | ||
| Potential detection of mineral water springs using statistical models (a case study of Vazroud Watershed, Mazandaran) | ||
| نویسندگان [English] | ||
| Karim Solaimani1؛ Ali Reza Motavali2 | ||
| 1University Academic member | ||
| چکیده [English] | ||
| Determining the potential of mineral springs is one of the important and essential issues in water resources management. In this study, the potential map of the spring for the management and planning of groundwater resources was performed using statistical models of frequency ratio (FR) and weight of evidence (WOE) in the Vazrood Watershed. For this purpose, 57 springs were identified randomly in the calibration (34) and validation (23) phases. In the implementation of both models, the effective factors in spring potential were used include: slope percentage and aspect, hypsometry, plan and profile, geology, land use, topographic wetness index, distance from fault, fault density, drainge density, distance from the river and the road. The results showed that the accuracy of the FR model was 84.4% and the accuracy of the WOE model was 77.2%. These results indicate a very good accuracy of these two models in determining the potential areas of springs in the Vazrood Watershed. According to the results, the accuracy of the FR model is also higher than the WOE model. Finally, 39.7% and 34.9% of Vazroud watershed in high and very high classes in terms of spring potential are in two models of WOE and FR models, respectively. Finally, the potential maps of the springs can be used to provide the basic infrastructure to clean the springs from human damage and the entry of investors to build a drinking water spring factory and ultimately the prosperity of the local economy of the Watershed residents of Vazroud. | ||
| کلیدواژه‌ها [English] | ||
| Spring. frequency ratio, the weight of evidence, Vazrood | ||
| مراجع | ||
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[1]. Badisar, Seyed Nasreddin., Ahmadi, Seyed Mohammad Sadegh., Modbarnejad, Atefeh Sadat. Evaluation of indicators of good governance in the water sector. Journal of Environmental Science and Technology. 2020; 22 (2), 275-286.
[2]. Mahdavi, M. Applied Hydrology. Volume One, University of Tehran. 2009; 342 pages.
[3]. Telvari, A. Groundwater Textbook. 2012; 186 pages.
[4]. Seif, A., Kargar, A. Groundwater potential identification using hierarchical analysis method and geographical system, Case study: Sirjan catchment. Journal of Natural Geography. 2011; 12 (4): 75-90.
[5]. Jaafari, A., et al. "GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran." International Journal of Environmental Science and Technology. 2014; 11(4): 909-926.
[6]. Naghibi, Seyed Amir, Hamid Reza Pourghasemi, Zohre Sadat Pourtaghi, and Ashkan Rezaei. "Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran." Earth Science Informatics. 2015; 8(1):171-186.
[7]. Zabihi, Mohsen., Pourghasemi, Hamid Reza., Behzadfar, Morteza. Preparation of groundwater potential map using Shannon entropy models and random forest in Bojnourd plain. Echo Hydrology. 2015; 2(2): From page 221 to page 232.
[8]. Zandi, Siran., Soleimani, Karim., Zandi, Jalal. Mapping of areas with potential for groundwater occurrence using logistic regression statistical method in GIS environment (Case study: Mirdeh mountain watershed, Kurdistan). Journal of Watershed Management (Scientific-Research. 2015; 6 (12): 75-87.
[9]. Mogaji, K. A., H. S. Lim, and K. Abdullah. "Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model." Arabian Journal of Geosciences. 2015;8(5): 3235-3258.
[10]. Waikar, M. L., & Nilawar, A. P. Identification of groundwater potential zone using remote sensing and GIS technique. International Journal of Innovative Research in Science, Engineering and Technology. 2014; 3(5): 12163-12174.
[11]. Chen, W., Zhao, X., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., Wang, X. and Ahmad, B.B., Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. Journal of Hydrology. 2020; 583, p.124602.
[12]. Nhu, V. H., Rahmati, O., Falah, F., Shojaei, S., Al-Ansari, N., Shahabi, H., ... & Ahmad, B. B. Mapping of groundwater spring potential in karst aquifer system using novel ensemble bivariate and multivariate models. Water. 2020; 12(4): 985.
[13]. Chen, W., Li, Y., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., Bian, H., Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models. Appl. Sci. 2020;. 10(2), 425
[14]. Tolche, A.D. Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia. Geol. Ecol. Landscapes. 2021; 5: 65–80.
[15]. Tabarestan Space Consulting Engineers Co. Detailed studies of Vazrood watershed in Noor city. Meteorological report. 2008; Pages 1-130.
[16]. Tabarestan Space Consulting Engineers Co.. Detailed studies of Vazrood watershed in Noor city. Vegetation report. 2008; Pages 1-61.
[17]. Tabarestan Space Consulting Engineers Co. Detailed studies of Vazrood watershed in Noor city. Erosion and sediment report. 2008; Pages 1-77.
[18]. Tabarestan Space Consulting Engineers Co. Detailed studies of Vazrood watershed in Noor city. Geology and Geomorphology report. 2008; Pages 1-77.
[19]. Water Company of Mazandaran (WCM). Spring distribution in Mazandaran province. Office of Basic Studies of Water Resources in Mazandaran province. 2013.
[20]. Yilmaz, I. and Keskin, I. GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bulletin of Engineering Geology and the Environment. 2009; 68(4):.459-471.
[21]. Larsen, K. Generalized naive Bayes classifiers. ACM SIGKDD Explorations Newsletter. 2005; 7(1): 76-81. [22]. Wang, L. J., Guo, M., Sawada, K., Lin, J., & Zhang, J. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal. 2016; 20(1): 117-136.
[23]. Goetz, J. N., Alexander Brenning, Helene Petschko, and Philip Leopold. "Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling." Computers & geosciences. 2015; 81: 1-11. [24]. Guru, B., Seshan, K., & Bera, S. Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. Journal of King Saud University-Science. 2017; 29(3): 333-347.
[25]. Tehrany, M. S., Pradhan, B., & Jebur, M. N. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of hydrology. 2014; 512: 332-343.
[26]. Beguería, S. Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards. 2006; 37:315–329. https://doi.org/10.1007/s11069-005-5182-6
[27]. Quirós, E., Felicísimo, Á. M., & Cuartero, A. Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images. Sensors. 2009; 9(11): 9011-9028.
[28]. Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology. 2016; 259: 105-118.
[29]. Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J & Duan, Z. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of the total environment. 2018; 626: 1121-1135.
[30]. Bretzler, A., Lalanne, F., Nikiema, J., Podgorski, J., Pfenninger, N., Berg, M., & Schirmer, M. Groundwater arsenic contamination in Burkina Faso, West Africa: predicting and verifying regions at risk. Science of the Total Environment. 2017; 584: 958-970.
[31]. Tziritis, E., Panagopoulos, A., Arampatzis, G. Development of an operational index of water quality (PoS) as a versatile tool to assist groundwater resources management and strategic planning. J Hydrol. 2014; 517: 339–350.
https://doi.org/10.1016/j.jhydrol.2014.05.008
[32]. Motevalli, A., Naghibi, S.A., Hashemi, H., Berndtsson, R., Pradhan, B. and Gholami, V. Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater. Journal of cleaner production. 2019; 228:1248-1263.
[33]. Kouli, M., C. Loupasakis, P. Soupios, D. Rozos, and F. Vallianatos. "Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece." Environmental earth sciences. 2014; 72(12): 5197-5219.
[34]. Arab Ameri, Alireza., Shirani, Kourosh., Rezaei, Khalil. Comparative evaluation of probabilistic methods of incident weight and frequency ratio in landslide risk zoning (Case study: Vanak watershed, Isfahan). Watershed Management Research Journal. 2015; 8 (15): 147 - 160.
[35]. Entezari, Mojgan., Jalilian, Tahereh., Darvish Khatouni, Javad. Zoning of flood susceptibility map using evaluation between frequency ratio method and evidence weight in Kermanshah province. Spatial analysis of environmental hazards. 2019; 6 (4): 143-162.
[36]. Guru, B., Seshan, K. and Bera, S. Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. Journal of King Saud University-Science. 2017; 29(3):333-347.
[37]. Krause, S., Jacobs, J. and Bronstert, A. Modelling the impacts of land-use and drainage density on the water balance of a lowland–floodplain landscape in northeast Germany. Ecological Modelling. 2007. 200(3-4): 475-492.
[38]. Rajaveni, S.P., Brindha, K. and Elango, L. Geological and geomorphological controls on groundwater occurrence in a hard rock region. Applied Water Science. 2017; 7(3):1377-1389.
[39]. Kazemi, Rahim., Ghayoumian, Jafar., Jalali, Nader. Investigation of the role of structural factors in the abundance of water resources in the karst Lar area using remote sensing and GIS. Research and construction in natural resources. 2006; 73: 33-41.
[40]. Ozdemir, A. 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. J Hydrol. 2011;411:290–308
[41]. Gurrieri, J.T. Rangeland water developments at springs: best practices for design, rehabilitation, and restoration. Gen. Tech. Rep. RMRS-GTR-405. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.2020; 21 p., 405.
[42]. Binkley, D. Management impacts on water quality of forests and rangelands (Vol. 239). US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 1993. | ||
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