Faculty Publications
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Item Assessing the changing pattern of hydro-climatic variables in the Aghanashini River watershed, India(Springer Science and Business Media Deutschland GmbH, 2023) Yashas Kumar, H.K.; Varija, K.Growing population and climate change have altered the hydro-climatic trend from past decades. This manuscript analyses the abrupt shift in these time series and their changing pattern using historical data sets. The Pettitt test and the Standard Normal Homogeneity Test were used to evaluate the time series' homogeneity. The Concentration Index, Precipitation Concentration Index and Seasonality Index were employed to analyse the spatial variability of daily, monthly and seasonal rainfall patterns over the Aghanashini River watershed. Furthermore, the temporal trend in the rainfall, streamflow, and temperature time series was investigated using Mann–Kendall (MK) and the graphical Innovative-Şen (IŞ) test. Clear evidence of climate change impact on the rainfall and streamflow pattern was recognized, as there is an upward shift in the maximum temperature time series and a downward shift in the rainfall and streamflow time series after 2001. The rainfall indices showed that the watershed has fewer percentage of rainy days and stronger rainfall seasonality, indicating a possible risk of flash floods in the downstream of the watershed. Additionally, the results of the MK and IŞ trend tests paralleled each other and provided support for the findings emphasized by rainfall indices. © 2023, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.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.