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K. P. Sudheer

Bio: K. P. Sudheer is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Hydrological modelling & Water resources. 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.


Papers
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Journal ArticleDOI
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.
Abstract: Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, 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. Consequently, there is still a need for the development of robust ANN model development approaches.

730 citations

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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.

568 citations

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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.
Abstract: A new approach for designing the network structure in an artificial neural network (ANN)-based rainfall-runoff model is presented. The method utilizes the statistical properties such as cross-, auto- and partial-auto-correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd.

394 citations

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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.
Abstract: Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.

308 citations

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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.
Abstract: This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing the daily values of ET for rice crop. Six RBF networks, each using varied input combinations of climatic variables, have been trained and tested. The model estimates are compared with measured lysimeter ET. The results of the study clearly demonstrate the proficiency of the ANN method in estimating the ET. The analyses suggest that the crop ET could be computed from air temperature using the ANN approach. However, the present study used a single crop data for a limited period, therefore further studies using more crops as well as weather conditions may be required to strengthen these conclusions.

267 citations


Cited by
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Journal ArticleDOI
TL;DR: A diagnostically interesting decomposition of NSE is presented, which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and it is shown how model calibration problems can arise due to interactions among these components.

3,147 citations

01 Apr 2003
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

2,975 citations

Journal ArticleDOI
TL;DR: The SWAT-CUP tool as discussed by the authors is a semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration, and is used to provide statistics for goodness-of-fit.
Abstract: SWAT (Soil and Water Assessment Tool) is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. Several calibration techniques have been developed for SWAT, including manual calibration procedures and automated procedures using the shuffled complex evolution method and other common methods. In addition, SWAT-CUP was recently developed and provides a decision-making framework that incorporates a semi-automated approach (SUFI2) using both manual and automated calibration and incorporating sensitivity and uncertainty analysis. In SWAT-CUP, users can manually adjust parameters and ranges iteratively between autocalibration runs. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. The user interaction or manual component of the SWAT-CUP calibration forces the user to obtain a better understanding of the overall hydrologic processes (e.g., baseflow ratios, ET, sediment sources and sinks, crop yields, and nutrient balances) and of parameter sensitivity. It is important for future calibration developments to spatially account for hydrologic processes; improve model run time efficiency; include the impact of uncertainty in the conceptual model, model parameters, and measured variables used in calibration; and assist users in checking for model errors. When calibrating a physically based model like SWAT, it is important to remember that all model input parameters must be kept within a realistic uncertainty range and that no automatic procedure can substitute for actual physical knowledge of the watershed.

2,200 citations

09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

2,069 citations