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 | 635 | 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 | 401 | 2008 |
Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool B Dixon Applied Geography 25 (4), 327-347, 2005 | 319 | 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 | 285 | 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 | 204 | 2013 |
Applications of fuzzy logic to the prediction of soil erosion in a large watershed B Mitra, HD Scott, JC Dixon, JM McKimmey Geoderma 86 (3-4), 183-209, 1998 | 189 | 1998 |
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 | 184 | 2018 |
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 | 157 | 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 | 147 | 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 | 131 | 2015 |
GIS and geocomputation for water resource science and engineering B Dixon, V Uddameri John Wiley & Sons, 2016 | 89 | 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 | 88 | 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 | 83 | 2012 |
Prediction of ground water vulnerability using an integrated GIS-based neuro-fuzzy techniques B Dixon Journal of Spatial Hydrology 4 (2), 2004 | 78 | 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 | 68 | 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 | 53 | 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 | 48 | 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 | 39 | 2008 |
Alternative spatially enhanced integrative techniques for mapping seagrass in Florida's marine ecosystem R Baumstark, B Dixon, P Carlson, D Palandro, K Kolasa International Journal of Remote Sensing 34 (4), 1248-1264, 2013 | 35 | 2013 |