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This study aims to model urban growth and land-use dynamics to predict changes in a Class-I urban agglomeration.

Modeling Land-Use Dynamics and Urban Growth Prediction Using an ANN-Based Cellular Automata–Markov Chain in Class-I Urban Agglomeration

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Randomized trial models urban growth prediction in Class-I urban agglomeration, suggesting effective management strategies for sustainable land use.

This study aims to model urban growth and land-use dynamics to predict changes in a Class-I urban agglomeration.
Used an artificial neural network (ANN) and cellular automata–Markov Chain for modeling.
Identified and tested six key drivers using Cramer’s V statistics.
Analyzed remotely sensed data and GIS for urban growth patterns over two decades.
Built-up area increased by 9.21 km² from 2001 to 2021.
Urban expansion projected to grow by 18.2% by 2030 and 47.44% by 2040.
Vegetation, agricultural land, fallow land, and water bodies are expected to decrease significantly by 2040.

Barman et al. (2026) studied this question.

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Randomized trial models urban growth prediction in Class-I urban agglomeration, suggesting effective management strategies for sustainable land use.