Barnali (Mitra) Dixon
Barnali (Mitra) Dixon
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Cited by
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), 1-27, 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
Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool
B Dixon
Applied Geography 25 (4), 327-347, 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
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
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
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
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
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
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
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
Prediction of ground water vulnerability using an integrated GIS-based neuro-fuzzy techniques.
BM Dixon
Spatial Hydrology, 2004
GIS and geocomputation for water resource science and engineering
B Dixon, V Uddameri
John Wiley & Sons, 2016
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
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), 1507-1520, 2009
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
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
Spatial interpolation of rainfall data using ArcGIS: A comparative study
J Earls, B Dixon
Proceedings of the 27th Annual ESRI International User Conference 31, 2007
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
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
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