최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Sustainability, v.9 no.7, 2017년, pp.1157 -
Park, Soyoung (BK21 Plus Project of the Graduate School of Earth Environmental Hazard System, Pukyong National University, Busan 48513, Korea) , Hamm, Se-Yeong (Department of Geological Sciences, Pusan National University, Busan 46241, Korea) , Jeon, Hang-Tak (Department of Geological Sciences, Pusan National University, Busan 46241, Korea) , Kim, Jinsoo (Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea)
This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well da...
Fitts, C.R. (2002). Groundwater Science, Academic Press.
Shahid Groundwater potential modeling in a soft rock area using a GIS Int. J. Remote Sens. 2000 10.1080/014311600209823 21 1919
10.1051/nss:2008056 Molden, D. (2007). Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture, IWMI & Earthscan.
Mannap Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS Arab. J. Geosci. 2014 10.1007/s12517-012-0795-z 7 711
Bera Ground water potential mapping in Dulung watershed using remote sensing & GIS techniques, West Bengal, India Int. J. Sci. Res. Publ. 2012 2 1
Sander Groundwater assessment using remote sensing and GIS in a rural groundwater project in Ghana: lessons learned Hydrogeol. J. 1996 10.1007/s100400050086 4 40
Singh, A.K., and Prakash, S.R. (2002, January 7-9). An integrated approach of remote sensing, geophysics and GIS to evaluation of groundwater potentiality of Ojhala sub-watershed, Mirjapur district, UP, India. Proceedings of the First Asian Conference on GIS, GPS, Aerial Photography and Remote Sensing, Bangkok, Thailand.
Waikar Identification of groundwater potential zone using remote sensing and GIS technique Int. J. Innov. Res. Sci. Eng. Technol. 2014 3 12163
10.1007/s10040-016-1466-z Naghibi, S.A., and Dashtpagerdi, M.M. (2016). Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeol. J., 1-21.
Elmahdy Probabilistic frequency ratio model for groundwater potential mapping in Al Jaww plain, UAE Arab. J. Geosci. 2015 10.1007/s12517-014-1327-9 8 2405
Naghibi Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran Earth Sci. Inform. 2015 10.1007/s12145-014-0145-7 8 171
Oh GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea J. Hydrol. 2011 10.1016/j.jhydrol.2010.12.027 399 158
Ozdemir Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey) J. Hydrol. 2011 10.1016/j.jhydrol.2011.05.015 405 123
Pourtaghi GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran Hydrogeol. J. 2014 10.1007/s10040-013-1089-6 22 643
Corsini Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy) Geomorphology 2009 10.1016/j.geomorph.2008.03.015 111 79
Adiat 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 J. Hydrol. 2012 10.1016/j.jhydrol.2012.03.028 440 75
Razandi Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS Earth Sci. Inform. 2015 10.1007/s12145-015-0220-8 8 867
Mogaji Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster-Shafer model Arab. J. Geosci. 2015 10.1007/s12517-014-1391-1 8 3235
Naghibi A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping Water Resour. Manag. 2015 10.1007/s11269-015-1114-8 29 5217
Nampak Application of GIS based data driven evidential belief function model to predict groundwater potential zonation J. Hydrol. 2014 10.1016/j.jhydrol.2014.02.053 513 283
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 Sustain. Water Resour. Manag. 2016 10.1007/s40899-016-0056-5 2 265
Yao A novel method for disease prediction: hybrid of random forest and multivariate adaptive regression splines J. Comput. 2013 10.4304/jcp.8.1.170-177 8 170
Hong Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models Geomorphology 2016 10.1016/j.geomorph.2016.02.012 259 105
Pham A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) Environ. Model. Softw. 2016 10.1016/j.envsoft.2016.07.005 84 240
Saito Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan Geomorphology 2009 10.1016/j.geomorph.2009.02.026 109 108
Trigila Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) Geomorphology 2015 10.1016/j.geomorph.2015.06.001 249 119
Wu Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China Environ. Earth Sci. 2014 10.1007/s12665-013-2863-4 71 4725
Shruthi Object-based gully system prediction from medium resolution imagery using random forests Geomorphology 2014 10.1016/j.geomorph.2014.04.006 216 283
Carranza Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) Comput. Geosci. 2015 10.1016/j.cageo.2014.10.004 74 60
Leite Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil Comput. Geosci. 2009 10.1016/j.cageo.2008.05.003 35 675
Artificial neural networks as a tool for mineral potential mapping with GIS Int. J. Remote Sens. 2003 10.1080/0143116021000031791 24 1151
Kisi Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution J. Hydrol. 2016 10.1016/j.jhydrol.2015.12.014 534 104
Lee Application of decision-tree model to groundwater productivity-potential mapping Sustainability 2015 10.3390/su71013416 7 13416
Rahmati Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran Catena 2016 10.1016/j.catena.2015.10.010 137 360
Gusyev Use of nested flow models and interpolation techniques for science-based management of the sheyenne national grassland, North Dakota, USA Groundwater 2013 10.1111/j.1745-6584.2012.00989.x 51 414
Xu Use of machine learning methods to reduce predictive error of groundwater models Groundwater 2014 10.1111/gwat.12061 52 448
Yoon A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer J. Hydrol. 2011 10.1016/j.jhydrol.2010.11.002 396 128
10.1007/s00704-016-2022-4 Naghibi, S.A., Pourghasemi, H.R., and Abbaspour, K. (2017). A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theor. Appl. Climatol., 1-18.
Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia Landslides 2016 10.1007/s10346-015-0593-2 13 671
Conoscenti Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy) Geomorphology 2015 10.1016/j.geomorph.2014.09.020 242 49
Wang Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models Catena 2015 10.1016/j.catena.2015.08.007 135 271
Eker Evaluation and comparison of landslide susceptibility mapping methods: A case study for the Ulus district, Bartın, northern Turkey Int. J. Geogr. Inf. Sci. 2015 10.1080/13658816.2014.953164 29 132
Buyeo-gun office (2016). Statistical Yearbook of Buyeo-Gun, Buyeo-gun.
Ministry of Environment (2016). Groundwater Annual Report.
Aniya Landslide-susceptibility mapping in the Amahata River basin, Japan Ann. Assoc. Am. Geogr. 1985 10.1111/j.1467-8306.1985.tb00061.x 75 102
Moore Digital terrain modelling: A review of hydrological, geomorphological, and biological applications Hydrol. Process. 1991 10.1002/hyp.3360050103 5 3
Wischmeier, W.H., and Smith, D.D. (1978). Predicting Rainfall Erosion Losses: A Guide to Conservation Planning.
Moore Sediment transport capacity of sheet and rill flow: application of unit stream power theory Water Res. 1986 10.1029/WR022i008p01350 22 1350
Gopinath Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India Int. J. Remote Sens. 2007 10.1080/01431160601086050 28 5583
Koike Construction and analysis of interpreted fracture planes through combination of satellite-image derived lineaments and digital elevation model data Comput. Geosci. 1998 10.1016/S0098-3004(98)00021-1 24 573
Friedman Lineament, linear, lineation: Some proposed new standards for old terms Geol. Soc. Am. Bull. 1976 10.1130/0016-7606(1976)87<1463:LLLSPN>2.0.CO;2 87 1463
Cuartero Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study Landslides 2013 10.1007/s10346-012-0320-1 10 175
Schnabel Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies Ecol. Model. 2009 10.1016/j.ecolmodel.2009.06.020 220 3630
Friedman Multivariate adaptive regression splines Ann. Stat. 1991 19 1
Zhang Multivariate adaptive regression splines application for multivariate geotechnical problems with big data Geotech. Geol. Eng. 2016 10.1007/s10706-015-9938-9 34 193
Zabihi GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran Environ. Earth Sci. 2016 10.1007/s12665-016-5424-9 75 1
Menard, S. (1995). Applied Logistic Regression Analysis, SAGE. [2nd ed.].
Ozdemir A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey J. Asian Earth Sci. 2013 10.1016/j.jseaes.2012.12.014 64 180
Milborrow, S. (2017, June 23). Notes on the Earth Package. Available online: https://www.milbo.org/doc/earth-varmod.pdf.
Swets Measuring the accuracy of diagnostic systems Science 1973 10.1126/science.3287615 240 1285
Bui Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS Comput. Geosci. 2012 10.1016/j.cageo.2011.10.031 45 199
Yesilnacar Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey) Eng. Geol. 2005 10.1016/j.enggeo.2005.02.002 79 251
Kennison Health and functional limitations predict depression scores in the health and retirement study: Results straight from MARS Calif. J. Health Promot. 2013 10.32398/cjhp.v11i1.1522 11 97
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.