Faculty Publications
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Item Generation of intensity duration frequency curve using daily rainfall data for aghanashini river watershed, uttara kannada(Springer Science and Business Media Deutschland GmbH, 2021) Nyamathi, S.J.; Yashas Kumar, H.K.Most of water resource projects are carried out after analysis of rainfall data. Intensity–duration–frequency (IDF) curves are used to analyze the quantity of rainfall of different duration (t) and return periods (T). The study area in Aghanashini watershed lies between 74°18′15.95″ and 74°55′22.84″ E longitude and 14°15′26.21″-14°37′17.65″ N latitude. Area is about 1400.47 km2, and altitude ranges from zero meters to 784 meters above mean sea level. The river extends from Sirsi to Kumta of Uttara Kannada, Karnataka State where it reaches Arabian Sea. The daily rainfall data of nine stations collected from Directorate of Economics and Statistics Bengaluru for years 1998 to 2016 was gone through, and 24-h maximum annual rainfall data was extracted. Indian Meteorological Department (IMD) proposed formula is used to estimate rainfall values for various shorter duration such as 0.083, 0.167, 0.25, 0.5, 1, 2, 12, 24-h. Probability distributions is used to estimate maximum annual rainfall values for various duration (t) and return periods (T) and Chi-square test is carried out to check the best probability distribution. Chi-square test shows that normal distribution is best fit to calculate rainfall intensity (mm/h) for six stations (Balale, Nilkundi, Sirsi, Hittalahally, Tyagali, Katagal), Log-Pearson type III probability distribution is best fit for two stations (Bandal and Siddapur) and Log-Normal distribution for one station, i.e., Kumta in Uttara Kannada. © Springer Nature Singapore Pte Ltd 2021.Item Prediction of ground water levels in uplands of coastal tropical riparian using PSO-SVM(CESER Publications Post Box No. 113 Roorkee 247667, 2014) Hegde, A.V.; Das, V.S.; Nyamathi, S.J.Wetlands are considered the most biologically diverse of all ecosystems. For the efficient management and proper development of a coastal tropical riparian, the behavior of ground water levels in uplands should be studied. Depending on the water levels in uplands, the surface water level on the nearby wetlands also changes, and sometimes changes the entire structure of wetlands. Computational Intelligence techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) along with optimization techniques such as Particle Swarm Optimization (PSO) etc., can be used to develop a mathematical model for predicting the ground water levels in uplands. In the present paper, hybrid model PSO-SVM has been used with different kernel functions for the prediction of the ground water levels in the uplands of Padre Wetland in Surathkal, Mangalore, India. It was found that PSO-SVM tool with B-spline kernel function gives higher regression coefficients and also a stable model for the prediction of ground water levels in these uplands. © 2014 IJED.Item Enhancing infiltration rate predictions with hybrid machine learning and empirical models: addressing challenges in southern India(Springer Science and Business Media Deutschland GmbH, 2025) Ramaswamy, M.V.; Yashas Kumar, H.K.; Reddy, V.J.; Nyamathi, S.J.Despite the success of machine learning (ML) in many disciplines, its application in hydrology, especially in water-scarce regions, faces challenges due to the lack of interpretability and physical consistency. This study addresses these challenges by integrating empirical hydrological models with ML techniques to predict infiltration rates in water-scarce regions of southern India. Using data from 199 observations across 11 sites, including soil characteristics and infiltration measurements, traditional models such as Philip’s, Horton’s, and Kostiakov’s were parameterized and combined with artificial neural networks (ANNs) and the MissForest (MF) algorithm to form hybrid models. The results demonstrate that the hybrid models, particularly those integrating Philip’s model with ANN and multiple predictors, achieved substantial improvements in prediction accuracy, with R2 values ranging from 0.803 to 0.918, root mean-square error (RMSE) from 0.083 to 0.118 cm/min, and Legates’ coefficient of efficiency (LCE) from 0.575 to 0.717 across the target sites. In contrast, empirical models alone at the test sites show lower performance, with R2 ranging from 0.499 to 0.902, RMSE from 0.091 to 0.152 cm/min, and LCE from 0.46 to 0.728, underscoring the limitations of traditional empirical models and the enhancement achieved through ML integration. By leveraging the strengths of empirical models and ML, the hybrid approach improves predictive accuracy and provides a more robust understanding of infiltration dynamics. The hybrid models enable accurate predictions using minimal, readily accessible data, offering a practical solution for water resource management and soil conservation in semi-arid, data-scarce regions. This study demonstrates that blending empirical knowledge with ML algorithms not only improves accuracy but also retains physical interpretability, presenting an innovative solution to hydrological modeling challenges in water-scarce environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.