| Random forest classifier for remote sensing classification M Pal International journal of remote sensing 26 (1), 217-222, 2005 | 4269 | 2005 |
| An assessment of the effectiveness of decision tree methods for land cover classification M Pal, PM Mather Remote sensing of environment 86 (4), 554-565, 2003 | 1684 | 2003 |
| Support vector machines for classification in remote sensing M Pal, PM Mather International Journal of Remote Sensing 26 (5), 1007-1011, 2005 | 1441 | 2005 |
| Feature selection for classification of hyperspectral data by SVM M Pal, GM Foody Geoscience and Remote Sensing, IEEE Transactions on 48 (5), 2297-2307, 2010 | 1028 | 2010 |
| Assessment of the effectiveness of support vector machines for hyperspectral data M Pal, PM Mather Future Generation Computer Systems 20 (7), 1215-1225, 2004 | 309 | 2004 |
| Kernel-based extreme learning machine for remote-sensing image classification M Pal, AE Maxwell, TA Warner Remote Sensing Letters 4 (9), 853-862, 2013 | 222 | 2013 |
| Support vector machines‐based modelling of seismic liquefaction potential M Pal International journal for numerical and analytical methods in geomechanics …, 2006 | 220 | 2006 |
| Modelling pile capacity using Gaussian process regression M Pal, S Deswal Computers and Geotechnics 37 (7-8), 942-947, 2010 | 203 | 2010 |
| M5 model tree based modelling of reference evapotranspiration M Pal, S Deswal Hydrological Processes 23 (10), 1437-1443, 2009 | 202 | 2009 |
| Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA S Bhattacharya, TR Carr, M Pal Journal of Natural Gas Science and Engineering 33, 1119-1133, 2016 | 191 | 2016 |
| Support vector classifiers for land cover classification M Pal, PM Mather Arxiv preprint arXiv:0802.2138, 2008 | 175* | 2008 |
| Some issues in the classification of DAIS hyperspectral data M Pal, PM Mather International Journal of Remote Sensing 27 (14), 2895-2916, 2006 | 169 | 2006 |
| Ensemble of support vector machines for land cover classification M Pal International journal of remote sensing 29 (10), 3043-3049, 2008 | 165 | 2008 |
| Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data M Pal, GM Foody IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2012 | 154 | 2012 |
| Modeling pile capacity using support vector machines and generalized regression neural network M Pal, S Deswal Journal of Geotechnical and Geoenvironmental Engineering 134, 1021, 2008 | 139 | 2008 |
| Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data M Pal International Journal of Remote Sensing 27 (14), 2877-2894, 2006 | 137 | 2006 |
| M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey M Taghi Sattari, M Pal, H Apaydin, F Ozturk Water Resources 40 (3), 233-242, 2013 | 136 | 2013 |
| Performance evaluation of artificial neural network approaches in forecasting reservoir inflow MT Sattari, K Yurekli, M Pal Applied Mathematical Modelling 36 (6), 2649-2657, 2012 | 120 | 2012 |
| Prediction of groundwater quality indices using machine learning algorithms H Raheja, A Goel, M Pal Water Practice and Technology 17 (1), 336 - 351, 2022 | 115 | 2022 |
| Ground water quality classification by decision tree method in Ardebil region, Iran SM Saghebian, MT Sattari, R Mirabbasi, M Pal Arabian journal of geosciences 7 (11), 4767-4777, 2014 | 115 | 2014 |
Paul M. MatherEmeritus Professor of Geographical Information Science, University of NottinghamVerified email at nottingham.ac.uk
