K
K. P. Sudheer
Researcher at Indian Institute of Technology Madras
Publications - 118
Citations - 6824
K. P. Sudheer is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Artificial neural network & Hydrological modelling. The author has an hindex of 34, co-authored 105 publications receiving 5819 citations. Previous affiliations of K. P. Sudheer include Kerala State Council for Science, Technology and Environment & Indian Institute of Technology Delhi.
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Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
TL;DR: Despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues and there is still a need for the development of robust ANN model development approaches.
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A neuro-fuzzy computing technique for modeling hydrological time series
TL;DR: Results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc.
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A data-driven algorithm for constructing artificial neural network rainfall-runoff models
TL;DR: A new approach for designing the network structure in an artificial neural network (ANN)-based rainfall-runoff model is presented and indicates that it could significantly reduce the effort and computational time required in developing an ANN model.
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Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach
TL;DR: In this paper, the authors investigated the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India and reported that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well.
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Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
TL;DR: In this article, the authors examined the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data, and employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop.