| GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran SA Naghibi, HR Pourghasemi, B Dixon Environmental monitoring and assessment 188, 1-27, 2016 | 716 | 2016 |
| Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? B Dixon, N Candade International Journal of Remote Sensing 29 (4), 1185-1206, 2008 | 420 | 2008 |
| Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool B Dixon Applied Geography 25 (4), 327-347, 2005 | 327 | 2005 |
| Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis B Dixon Journal of hydrology 309 (1-4), 17-38, 2005 | 287 | 2005 |
| Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran E Fijani, AA Nadiri, AA Moghaddam, FTC Tsai, B Dixon Journal of hydrology 503, 89-100, 2013 | 216 | 2013 |
| Multiscale analyses and characterizations of surface topographies CA Brown, HN Hansen, XJ Jiang, F Blateyron, J Berglund, N Senin, ... CIRP annals 67 (2), 839-862, 2018 | 214 | 2018 |
| applications of fuzzy logic to the prediction of soil erosion in a large watershed JMMK B. Mitra, H.D Scott, J.C Dixon Geoderma 86 (3-4), Pages 183-209, 1998 | 195 | 1998 |
| Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow ML Tan, DL Ficklin, B Dixon, Z Yusop, V Chaplot Applied Geography 63, 357-368, 2015 | 171 | 2015 |
| Resample or not?! Effects of resolution of DEMs in watershed modeling B Dixon, J Earls Hydrological Processes: An International Journal 23 (12), 1714-1724, 2009 | 155 | 2009 |
| Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis M Ustuner, FB Sanli, B Dixon European Journal of Remote Sensing 48 (1), 403-422, 2015 | 142 | 2015 |
| GIS and geocomputation for water resource science and engineering B Dixon, V Uddameri John Wiley & Sons, 2016 | 104 | 2016 |
| Effects of urbanization on streamflow using SWAT with real and simulated meteorological data B Dixon, J Earls Applied Geography 35 (1-2), 174-190, 2012 | 92 | 2012 |
| Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs P Samui, B Dixon Hydrological Processes 26 (9), 1361-1369, 2012 | 88 | 2012 |
| Prediction of ground water vulnerability using an integrated GIS-based neuro-fuzzy techniques B Dixon Journal of Spatial Hydrology 4 (2), 2004 | 79 | 2004 |
| A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N B Dixon Hydrogeology Journal 17 (6), 1507, 2009 | 72 | 2009 |
| Spatial interpolation of rainfall data using ArcGIS: A comparative study J Earls, B Dixon Proceedings of the 27th Annual ESRI International User Conference 31, 1-9, 2007 | 56 | 2007 |
| Prediction of aquifer vulnerability to pesticides using fuzzy rule-based models at the regional scale B Dixon, HD Scott, JC Dixon, KF Steele Physical geography 23 (2), 130-153, 2002 | 52 | 2002 |
| Multispectral classification of Landsat images: a comparison of support vector machine and neural network classifiers N Candade, B Dixon ASPRS Annual Conference Proceedings, Denver, Colorado, 2004 | 49 | 2004 |
| A comparison of SWAT model‐predicted potential evapotranspiration using real and modeled meteorological data J Earls, B Dixon Vadose Zone Journal 7 (2), 570-580, 2008 | 43 | 2008 |
| Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches M Bordbar, A Neshat, S Javadi, B Pradhan, B Dixon, S Paryani Natural Hazards, 1-22, 2022 | 37 | 2022 |
John C DixonUniversity of arkansas在 uark.edu 的电子邮件经过验证
John C DixonUniversity of arkansas在 uark.edu 的电子邮件经过验证