Faculty Publications
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Item Impact of recent floods on river morphology of Upper Krishna River: a decadal analysis using remote sensing approach(Springer Science and Business Media Deutschland GmbH, 2024) Choudhary, P.; Azhoni, A.; Devatha, C.P.Alluvial rivers are dynamic landscapes on the earth’s surface that evolve with time. While many studies have examined the immediate effects of floods on river channels, there is a lack of research that investigates the longer-term evolution of river morphology following such events. The present study was carried out on the Upper Krishna River which flows between the southern part of Maharashtra and the northern part of Karnataka states in India for 375 Km. The morphological parameters were analyzed for three decades (1991–2021) and the year 2019 with the highest flood level was also considered for change analysis. The assessment was done for change in active channel area, mean width, bank line migration, sinuosity index, and erosion-accretion. The land use classification was also analyzed for the study period to understand the exposure to future floods. The spatial data was retrieved from different satellite missions and analyzed with the help of Remote Sensing (RS) and Geographical Information System (GIS). The river was divided into seven segments (R1, R2, R3, R4, R5, R6, and R7) and bank lines were digitised manually to minimise possible errors. The results show that during the study period, the river channel has been modified in terms of active channel area expansion in the R1, R5, R6, and R7, and erosion was found the dominating process while the left bank was more erosive than the right bank of the river. The built-up area was seen going through a major expansion than any other land use class. The discharge and sediment data confirm the flood years (1994, 2005, 2006, and 2019) which accelerated the morphological activity in the river segment. The results of the study provide new insights related to short-term morphological changes in the Upper Krishna River and can be used by policymakers and managers to carry out future development plans and river training work at affected sites. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.Item Integration of multi-layer perceptron neural network and cellular Automata-Markov chain approach for the prediction of land use land cover in land change modeler(Elsevier B.V., 2025) Choudhary, P.; Devatha, C.P.; Azhoni, A.Land use and land cover (LULC) significantly influence the hydrological cycle and various earth processes. Understanding these dynamics is essential for effectively managing environmental issues within river basins. The study focuses on a highly dynamic and flood-prone sub-basin of the Upper Krishna River, where major urban settlements and intensive agricultural activities are concentrated along the riverbanks. The uniqueness of this research comes from the selection of this hydrologically sensitive landscape, shaped by both natural processes and anthropogenic pressures, which presents a critical case for land use and land cover modeling. Utilizing high-resolution satellite data (10 m), combined with the advanced Multi-Layer Perceptron Neural Networks (MLPNN) and Cellular Automata-Markov Chain (CA-Markov) modeling techniques within TerrSet's Land Change Modeler (LCM), which is not only capable of generating spatial transitions and dynamic maps but also identifies the key contributors in gain and loss of various land use classes. We projected LULC scenarios for the mid-century (2049) and end-century (2099) using data from 2015 to 2020. Our model was validated against the actual LULC map from 2024 and showed a strong correlation (Kappa = 0.85). The results indicate significant urban growth along the riverbank and predict an increase in built-up area from 6.53 % in 2024 to 9.59 % in 2049 and further to 15 % by 2099 of the total geographical area. We observed consistent declines in forest cover, cropland, and barren land. These findings are valuable for future hydrological studies and provide important insights for policymakers to support sustainable urban planning and flood risk management. © 2025