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Momcilo Markus
Momcilo Markus
Verified email at illinois.edu
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Cited by
Cited by
Year
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
7182000
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
6782000
Precipitation-runoff modeling using artificial neural networks and conceptual models
AS Tokar, M Markus
Journal of Hydrologic Engineering 5 (2), 156-161, 2000
4862000
Impacts of urbanization and climate variability on floods in Northeastern Illinois
MI Hejazi, M Markus
Journal of Hydrologic Engineering 14 (6), 606-616, 2009
1242009
Streamflow forecasting based on artificial neural networks
JD Salas, M Markus, AS Tokar
Artificial neural networks in hydrology, 23-51, 2000
1042000
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
1032003
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
932015
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
902002
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
852010
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
762007
Hydroinformatics: Data Integrative Approaches in Computation
P Kumar, J Alameda, P Bajcsy, M Folk, M Markus
Analysis and Modeling. CRC Press, 1-534, 2006
59*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
502017
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
502014
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
482007
Analysis of a changing hydrologic flood regime using the Variable Infiltration Capacity model
D Park, M Markus
Journal of Hydrology 515, 267-280, 2014
472014
Predicting streamflows based on neural networks
M Markus, JD Salas, HS Shin
Proceedings of the 1st International Conference on Water Resources. Part 1 …, 1995
461995
Development of error correction techniques for nitrate-N load estimation methods
S Verma, M Markus, RA Cooke
Journal of Hydrology 432, 12-25, 2012
412012
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
362010
Predictability of annual sediment loads based on flood events
M Markus, M Demissie
Journal of Hydrologic Engineering 11 (4), 354-361, 2006
342006
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
292003
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