| Artificial neural networks in hydrology. I: Preliminary concepts ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Journal of Hydrologic Engineering 5 (2), 115-123, 2000 | 721 | 2000 |
| Artificial neural networks in hydrology. II: Hydrologic applications ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Journal of Hydrologic Engineering 5 (2), 124-137, 2000 | 694 | 2000 |
| Precipitation-runoff modeling using artificial neural networks and conceptual models AS Tokar, M Markus Journal of Hydrologic Engineering 5 (2), 156-161, 2000 | 505 | 2000 |
| Impacts of urbanization and climate variability on floods in Northeastern Illinois MI Hejazi, M Markus Journal of Hydrologic Engineering 14 (6), 606-616, 2009 | 130 | 2009 |
| Streamflow forecasting based on artificial neural networks JD Salas, M Markus, AS Tokar Artificial neural networks in hydrology, 23-51, 2000 | 109 | 2000 |
| Climate change impacts on flow, sediment and nutrient export in a Great Lakes watershed using SWAT S Verma, R Bhattarai, NS Bosch, RC Cooke, PK Kalita, M Markus CLEAN–Soil, Air, Water 43 (11), 1464-1474, 2015 | 104 | 2015 |
| Entropy and generalized least square methods in assessment of the regional value of streamgages M Markus, HV Knapp, GD Tasker Journal of hydrology 283 (1-4), 107-121, 2003 | 104 | 2003 |
| Uncertainty of nitrate‐N load computations for agricultural watersheds Y Guo, M Markus, M Demissie Water Resources Research 38 (10), 3-1-3-12, 2002 | 92 | 2002 |
| Using chloride and other ions to trace sewage and road salt in the Illinois Waterway WR Kelly, SV Panno, KC Hackley, HH Hwang, AT Martinsek, M Markus Applied Geochemistry 25 (5), 661-673, 2010 | 90 | 2010 |
| The accuracy of sediment loads when log-transformation produces nonlinear sediment load–discharge relationships DW Crowder, M Demissie, M Markus Journal of Hydrology 336 (3-4), 250-268, 2007 | 77 | 2007 |
| Hydroinformatics: Data Integrative Approaches in Computation P Kumar, J Alameda, P Bajcsy, M Folk, M Markus Analysis and Modeling. CRC Press, 1-534, 2006 | 54* | 2006 |
| Modeling nonstationary extreme value distributions with nonlinear functions: An application using multiple precipitation projections for US cities MJ Um, Y Kim, M Markus, DJ Wuebbles Journal of Hydrology 552, 396-406, 2017 | 53 | 2017 |
| Analysis of a changing hydrologic flood regime using the Variable Infiltration Capacity model D Park, M Markus Journal of Hydrology 515, 267-280, 2014 | 52 | 2014 |
| Sensitivity analysis of annual nitrate loads and the corresponding trends in the lower Illinois River M Markus, M Demissie, MB Short, S Verma, RA Cooke Journal of Hydrologic Engineering 19 (3), 533-543, 2014 | 52 | 2014 |
| Changing estimates of design precipitation in Northeastern Illinois: Comparison between different sources and sensitivity analysis M Markus, JR Angel, L Yang, MI Hejazi Journal of hydrology 347 (1-2), 211-222, 2007 | 48 | 2007 |
| Predicting streamflows based on neural networks M Markus, JD Salas, HS Shin Proceedings of the 1st International Conference on Water Resources. Part 1 …, 1995 | 48 | 1995 |
| Development of error correction techniques for nitrate-N load estimation methods S Verma, M Markus, RA Cooke Journal of Hydrology 432, 12-25, 2012 | 44 | 2012 |
| Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois M Markus, MI Hejazi, P Bajcsy, O Giustolisi, DA Savic Journal of Hydroinformatics 12 (3), 251-261, 2010 | 43 | 2010 |
| Predictability of annual sediment loads based on flood events M Markus, M Demissie Journal of Hydrologic Engineering 11 (4), 354-361, 2006 | 35 | 2006 |
| Uncertainty of weekly nitrate-nitrogen forecasts using artificial neural networks M Markus, CWS Tsai, M Demissie Journal of environmental engineering 129 (3), 267-274, 2003 | 32 | 2003 |
James R. AngelIllinois State Water Survey確認したメール アドレス: illinois.edu
