| Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records RSV Teegavarapu, V Chandramouli Journal of hydrology 312 (1-4), 191-206, 2005 | 515 | 2005 |
| Deriving a general operating policy for reservoirs using neural network H Raman, V Chandramouli Journal of Water Resources Planning and Management 122 (5), 342-347, 1996 | 251 | 1996 |
| Multireservoir modeling with dynamic programming and neural networks V Chandramouli, H Raman Journal of Water Resources Planning and Management 127 (2), 89-98, 2001 | 193 | 2001 |
| Water quality assessment of an untreated effluent impacted urban stream: the Bharalu tributary of the Brahmaputra River, India TR Girija, C Mahanta, V Chandramouli Environmental monitoring and assessment 130, 221-236, 2007 | 151 | 2007 |
| A fuzzy neural network model for deriving the river stage—discharge relationship P Deka, V Chandramouli Hydrological sciences journal 48 (2), 197-209, 2003 | 79 | 2003 |
| Neural network based decision support model for optimal reservoir operation V Chandramouli, P Deka Water resources management 19, 447-464, 2005 | 69 | 2005 |
| Fuzzy neural network model for hydrologic flow routing P Deka, V Chandramouli Journal of Hydrologic Engineering 10 (4), 302-314, 2005 | 60 | 2005 |
| Radioimmunoassays specific for the tertiary and primary structures of thyroxine-binding globulin (TBG): measurement of denatured TBG in serum S Refetoff, Y MURATA, G Vassart, V CHANDRAMOULI, JS MARSHALL The Journal of Clinical Endocrinology & Metabolism 59 (2), 269-277, 1984 | 60 | 1984 |
| Fuzzy neural network modeling of reservoir operation PC Deka, V Chandramouli Journal of water resources planning and management 135 (1), 5-12, 2009 | 54 | 2009 |
| Backfilling missing microbial concentrations in a riverine database using artificial neural networks V Chandramouli, G Brion, TR Neelakantan, S Lingireddy Water Research 41 (1), 217-227, 2007 | 48 | 2007 |
| Predicting total organic carbon removal efficiency and coagulation dosage using artificial neural networks S Dharman, V Chandramouli, S Lingireddy Environmental Engineering Science 29 (8), 743-750, 2012 | 20 | 2012 |
| Robust training termination criterion for back-propagation ANNs applicable to small data sets V Chandramouli, S Lingireddy, GM Brion Journal of computing in civil engineering 21 (1), 39-46, 2007 | 11 | 2007 |
| Relative performance of artificial neural networks and regression models in predicting missing water quality data P Tyagi, V Chandramouli, S Lingireddy, D Buddhi Environmental engineering science 25 (5), 657-668, 2008 | 9 | 2008 |
| Study on water sharing in a multi-reservoir system using a dynamic programming-neural network model V Chandramouli, KA Kuppusamy, K Manikandan International Journal of Water Resources Development 18 (3), 425-438, 2002 | 9 | 2002 |
| Predicting enteric virus presence in surface waters using artificial neural network models V Chandramouli, TR Neelakantan, GM Brion, S Lingireddy Environmental engineering science 25 (1), 53-62, 2008 | 7 | 2008 |
| Urban flood hazard mitigation of Guwahati city by silt monitoring and watershed modeling AK Sarma, V Chandramouli, B Singh, P Goswami, N Rajbongshi Report submitted to Ministry of Human Resources Department (MHRD) by Dept …, 2005 | 7 | 2005 |
| Generalized visualization modules for solute transport in groundwater V Chandramouli, M Narayana, V Duruvai, S Guo, J Moreland, V Merwade, ... World Environmental and Water Resources Congress 2011: Bearing Knowledge for …, 2011 | 6 | 2011 |
| Application of genetic algorithm to determine optimal cropping pattern AK Sarma, R Misra, V Chandramouli Opsearch 43, 320-329, 2006 | 6 | 2006 |
| Visualization modules for solute transport in groundwater V Chandramouli, CQ Zhou, L Jin, M Narayana, VK Duvruvai EWRI Congress, 2010 | 5 | 2010 |
| Optimal operation of multi-reservoir system using dynamic programming and neural network H Raman, V Chandramouli WIT Transactions on Information and Communication Technologies 16, 2024 | 4 | 2024 |
