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Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

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Growing agricultural, industrial, and residential needs have increased the demand for groundwater resources. Targeting groundwater has become a challenging endeavour because of the complex interplay between varying climatic, geological, hydrological, and physiographic elements. This study proposes a hybrid RS, GIS, and ANP method to delineate groundwater zones. The resource was evaluated using seven surface hydrological and six subsurface aquifer parameters. The analytic network process model was used to determine the global priority vectors of each subclass. Surface and subsurface groundwater potential maps were created by assigning the resulting weights and spatially integrating them. Later, an integrated potential map was created by combining them. The validation of the obtained results using water level data demonstrates that the integrated map accurately predicted the zones. The area under study has 172.94 km2 of good groundwater potential. An area of 393.01 km2 is classified as having a moderate potential, and an area of 410 km2 is classified as having low potential. These findings will be beneficial to regional policymaking and long-term groundwater management. The results show that an integrated approach using ANP can better determine the groundwater potential zones in semi-arid zones.

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