| Support vector machine applications in the field of hydrology: a review S Raghavendra N, PC Deka Applied Soft Computing 19, 372-386, 2014 | 778 | 2014 |
| An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs AP Patil, PC Deka Computers and Electronics in Agriculture 121, 385-392, 2016 | 149 | 2016 |
| Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting M Rezaie-balf, SR Naganna, A Ghaemi, PC Deka Journal of hydrology 553, 356-373, 2017 | 144 | 2017 |
| A basic review of fuzzy logic applications in hydrology and water resources S Kambalimath, PC Deka Applied Water Science 10 (8), 1-14, 2020 | 133 | 2020 |
| Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time PC Deka, R Prahlada Ocean Engineering 43, 32-42, 2012 | 133 | 2012 |
| Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms SR Naganna, PC Deka, MA Ghorbani, SM Biazar, N Al-Ansari, ... Water 11 (4), 742, 2019 | 95 | 2019 |
| 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 |
| Remote sensing and GIS application in assessment of coastal vulnerability and shoreline changes: a review KSS Parthasarathy, PC Deka ISH Journal of Hydraulic Engineering 27 (sup1), 588-600, 2021 | 77 | 2021 |
| Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression N Sujay Raghavendra, PC Deka Cogent Engineering 2 (1), 999414, 2015 | 70 | 2015 |
| Neural network based decision support model for optimal reservoir operation V Chandramouli, P Deka Water resources management 19, 447-464, 2005 | 69 | 2005 |
| Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale S Gadad, PC Deka Applied Energy 176, 157-170, 2016 | 66 | 2016 |
| Sustainable development and management of groundwater resources in mining affected areas: a review NS Raghavendra, PC Deka Procedia Earth and Planetary Science 11, 598-604, 2015 | 63 | 2015 |
| Fuzzy neural network model for hydrologic flow routing P Deka, V Chandramouli Journal of Hydrologic Engineering 10 (4), 302-314, 2005 | 60 | 2005 |
| Forecasting of time series significant wave height using wavelet decomposed neural network R Prahlada, PC Deka Aquatic Procedia 4, 540-547, 2015 | 58 | 2015 |
| Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach P Kulithalai Shiyam Sundar, PC Deka Environmental Science and Pollution Research 29 (57), 86220-86236, 2022 | 56 | 2022 |
| 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 |
| Hybrid wavelet packet machine learning approaches for drought modeling P Das, SR Naganna, PC Deka, J Pushparaj Environmental Earth Sciences 79 (10), 221, 2020 | 52 | 2020 |
| Factors influencing streambed hydraulic conductivity and their implications on stream–aquifer interaction: a conceptual review SR Naganna, PC Deka, S Ch, WF Hansen Environmental Science and Pollution Research 24, 24765-24789, 2017 | 51 | 2017 |
| Evaluating the performance of CHIRPS satellite rainfall data for streamflow forecasting B Sulugodu, PC Deka Water Resources Management 33, 3913-3927, 2019 | 43 | 2019 |
| Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India AP Patil, PC Deka Neural Computing and Applications, 1-11, 2015 | 42 | 2015 |
V.ChandramouliAdresă de e-mail confirmată pe pnw.edu
