Faculty Publications
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Publications by NITK Faculty
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Item Developing regression models for predicting pan evaporation from climatic data - A comparison of multiple least-squares, principal components, and partial least-squares approaches(2007) Kovoor, G.M.; Nandagiri, L.Regression models for predicting daily pan evaporation depths from climatic data were developed using three multivariate approaches: multiple least-squares regression (MLR), principal components regression (PCR), and partial least-squares (PLS) regression. The objective was to compare the prediction accuracies of regression models developed by these three approaches using historical climatic datasets of four Indian sites that are located in distinctly different climatic regimes. In all cases (three approaches applied to four climatic datasets), regression models were developed using a part of the data and subsequently validated with the remaining data. Results indicated that although performances of the regression models varied from one climate to another, more or less similar prediction accuracies were obtained by all three approaches, and it was difficult to identify the best approach based on performance statistics. However, the final forms of the regression models developed by the three approaches differed substantially from one another. In all cases, the models derived using PLS contained the smallest number of predictor variables; between two to three out of a possible maximum of six predictor variables. The MLR approach yielded models with three to six predictor variables, and PCR models included all six predictor variables. This implies that the PLS regression models are the most parsimonious in terms of input data required for estimating epan from climate variables, and yet yield predictions that are almost as accurate as the more data-intensive MLR and PCR models. © 2007 ASCE.Item Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach(IWA Publishing, 2023) Hiremath, C.G.; Nandagiri, L.The present study on the hydrologic regionalization was taken up to evaluate the utility of hierarchical cluster analysis for the delineation of hydrologically homogeneous regions and multiple linear regression (MLR) models for information transfer to derive flow duration curve (FDC) in ungauged basins. For this purpose, 50 catchments with largely unregulated flows located in South India were identified and a dataset of historical streamflow records and 16 catchment attributes was created. Using selected catchment attributes, three hydrologically homogenous regions were delineated using a hierarchical agglomerative cluster approach, and nine flow quantiles (10–90%) for each of the catchments in the respective clusters was derived. Regionalization approach was then adopted, whereby using step-wise regression, flow quantiles were related with readily derived basin-physical characteristics through MLR models. Cluster-wise performance analysis of the developed models indicated excellent performance with an average coefficient of determination (R2) values of 0.85, 0.97, and 0.8 for Cluster-1,-2, and-3, respectively, in comparison to poor performance when all 50 stations were considered to be in a single region. However, Jackknife cross-validation showed mixed performances with regard to the reliability of developed models with performance being good for high-flow quantiles and poor for low-flow quantiles. © 2023 The Authors.