Assessment of Land Use and Land Cover Change of Visakhapatnam Using CA-ANN

Land use and land cover (LULC) change in urban areas play a fundamental role in altering terrestrial surface dynamics. Accurately predicting future LULC is a complex challenge that requires combining both temporal and spatial modeling techniques. Among the various methods available, the cellular automata and artificial neural networks (CA-ANN) model has shown effectiveness, due to its compatibility with remote sensing data and geographic information systems (GIS). This makes it especially suitable for fine-resolution modeling and simulating dynamic spatial changes. This chapter focuses on forecasting LULC changes in Visakhapatnam, the largest and most densely populated metropolitan city in the Indian state of Andhra Pradesh. The primary aim was to project LULC patterns for 2020 and 2030 using the CA-ANN model, based on historical data from 2000 and 2010. The model’s predictions were validated against 2020 LULC data, achieving a prediction accuracy of up to 90%. A wide range of socioeconomic, spatial, and environmental factors was incorporated, including slope, proximity to roads, water bodies, infrastructure, aspect, and elevation. The results revealed substantial changes, characterized by a marked expansion of built-up areas and a corresponding decline in agricultural and fallow lands. Additionally, urbanization exerted considerable pressure on forested regions and water bodies, leading to significant alterations in these land cover types. The Markov chain model demonstrated a prediction accuracy of 90%, underscoring its effectiveness in modeling spatial dynamics, while the CA-ANN model further reinforces its value in forecasting these spatial changes. These findings emphasize the critical need for sustainable land-use planning, encouraging policymakers to adopt responsible strategies for managing urban growth to mitigate environmental impacts.