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Dr K. S Kasiviswanathan

vidwan id: 155388
Male

Assistant Professor, Water Resources Development and Management
Indian Institute of Technology Roorkee

Expertise

  • Water Resources

Publications

Total Articles 16
Books 0
Proceedings 0

Publications

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Scopus

Citations 657
h-index 13

CrossRef

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Citations 449
h-index 10
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Bio

Hydrological Modelling , Reservoir operation; Flood and Drought Management; Optimization and Uncertainty Quantification

Personal Details

  • Male
  • Assistant Professor , Indian Institute of Technology Roorkee
  • Water Resources Development and Management, Indian Institute of Technology Roorkee
Ph.D
Indian Institute of Technology Madras 2014
Assistant Professor Dec 2019 – Present
Indian Institute of Technology Roorkee | Water Resources Development and Management

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Co-Authors (9)

Idhaya Chandhiran

Dr Idhaya Chandhiran Ilampooranan

Indian Institute of Technology Roorkee

Santosh Murlidhar

Dr Santosh Murlidhar Pingale

National Institute of Hydrology, Roorkee

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Scholarly Work

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Scholarly Publications

Implications of uncertainty in inflow forecasting on reservoir operation for irrigation

Open Access
Article
Authors: Kasiviswanathan K.S.;Sudheer K.P.;Soundharajan B.S.;Adeloye A.J.

Stationary hydrological frequency analysis coupled with uncertainty assessment under nonstationary scenarios

Open Access
Article
Authors: Vidrio-Sahagún C.T.;He J.;Kasiviswanathan K.S.;Sen S.

Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis

Open Access
Article

Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

Open Access
Article

Trends and non-stationarity in groundwater level changes in rapidly developing Indian cities

Open Access
Article
Authors: Mohanavelu A.;Kasiviswanathan K.S.;Mohanasundaram S.;Ilampooranan I.;He J.;Pingale S.M.;Soundharajan B.S.;Mohaideen M.M.D.

Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling

Open Access
Article

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

Open Access
Article

Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models

Open Access
Review