Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi-Layer Perceptron Neural Networks (MLPNN)

Land use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN-based on MLPNNs (multi-layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA-GIOVANNI MERRA-2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built-up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built-up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO₂, SO₂, SO₄, NO₂, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources.