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K S Kasiviswanathan
K S Kasiviswanathan
Associate Professor, IIT Roorkee, India
Verified email at wr.iitr.ac.in - Homepage
Title
Cited by
Cited by
Year
Potential application of wavelet neural network ensemble to forecast streamflow for flood management
KS Kasiviswanathan, J He, KP Sudheer, JH Tay
Journal of Hydrology 536, 161–173, 2016
1562016
Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes
S Ghosh, H Vittal, T Sharma, S Karmakar, KS Kasiviswanathan, ...
Plos One, 1-14, 2016
1382016
Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations
KS Kasiviswanathan, R Cibin, KP Sudheer, I Chaubey
Journal of Hydrology 499, 275-288, 2013
1002013
Quantification of the predictive uncertainty of artificial neural network based river flow forecast models
KS Kasiviswanathan, KP Sudheer
Stochastic environmental research and risk assessment 27, 137-146, 2013
982013
Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models
KS Kasiviswanathan, KP Sudheer
Stochastic Environmental Research and Risk Assessment, 1-12, 2016
93*2016
Genetic programming based monthly groundwater level forecast models with uncertainty quantification
KS Kasiviswanathan, S Saravanan, M Balamurugan, K Saravanan
Model. Earth Syst. Environ. 2 (27), 1-11, 2016
442016
COVID-19 Lockdown disruptions on water resources, wastewater, and agriculture in India
M Balamurugan, KS Kasiviswanathan, I Ilampooranan, BS Soundharajan
Frontiers in Water 3, 603531, 2021
362021
Quantification of Prediction Uncertainty in Artificial Neural Network Models
KS Kasiviswanathan, KP Sudheer, J He
Artificial Neural Network Modelling, Studies in Computational Intelligence …, 2016
242016
Flood frequency analysis using multi-objective optimization based interval estimation approach
KS Kasiviswanathan, J He, JH Tay
Journal of Hydrology, 2016
232016
Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis
S Sen, J He, KS Kasiviswanathan
Journal of hydrology 584, 124666, 2020
212020
A global-scale hydropower potential assessment and feasibility evaluations
WM Tefera, KS Kasiviswanathan
Water Resources and Economics 38, 100198, 2022
202022
Trends and Non-stationarity in groundwater level changes in rapidly developing Indian cities
A Mohanavelu, KS Kasiviswanathan, S Mohanasundaram, ...
Water 12 (11), 3209, 2020
182020
Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios
CT Vidrio-Sahagún, J He, KS Kasiviswanathan, S Sen
Journal of Hydrology 598, 125725, 2021
152021
Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models
KS Kasiviswanathan, KP Sudheer, J He
Hydrological sciences journal 63 (1), 101-113, 2018
152018
Radial basis function artificial neural network: Spread selection
KS Kasiviswanathan, A Agarwal
International Journal of Advanced Computer Science 2 (11), 394-398, 2012
152012
Assessment of the spatial–temporal distribution of groundwater recharge in data-scarce large-scale African river basin
AH Gelebo, KS Kasiviswanathan, D Khare
Environmental Monitoring and Assessment 194 (3), 157, 2022
132022
Implications of uncertainty in inflow forecasting on reservoir operation for irrigation
KS Kasiviswanathan, KP Sudheer, BS Soundharajan, AJ Adeloye
Paddy and water environment 19, 99-111, 2021
132021
Height–area–storage functional models for evaporation-loss inclusion in reservoir-planning analysis
AJ Adeloye, IY Wuni, QV Dau, BS Soundharajan, KS Kasiviswanathan
Water 11 (7), 1413, 2019
132019
Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling
KS Kasiviswanathan, J He, JH Tay, KP Sudheer
Water Resources Managment, 1-15, 2018
112018
Quantification of water resource sustainability in response to drought risk assessment for Afghanistan river basins
R Dost, KS Kasiviswanathan
Natural Resources Research 32 (1), 235-256, 2023
102023
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