Artificial Neural Network - Cellular Automata (ANN–CA) Modelling for Spatio-Temporal LULC Dynamics in Semi-Arid Akole Tehsil, Maharashtra

DOI

10.2991/978-94-6463-940-7_11How to use a DOI?

Keywords

Land Use Land Cover (LULC); Landsat; Artificial Neural Network (ANN); Cellular Automata (CA)

Abstract

This study investigates Land Use Land Cover (LULC) dynamics in ecologically sensitive Akole tehsil of Ahmednagar district over the past two decades (2001–2021) and predicts future scenarios for 2031 and 2041. Multi-temporal Landsat imagery (Landsat-5 and Landsat-8) acquired in February was classified using Maximum Likelihood Classifier (MLC) into five major classes: Water body, Built-up area, Vegetation, Agriculture, and Barren land. Change detection analysis revealed a substantial expansion of Agricultural land and Built-up area, accompanied by a decline in Barren land, with fluctuating Vegetation and Water body extents. Transition probabilities were quantified using an Artificial Neural Network–Multilayer Perceptron (ANN-MLPNN), while Cellular Automata (CA) modeling was employed for spatial prediction of future LULC. The simulation results indicate continued Agricultural expansion (468.53 km2 in 2021 to 604.17 km2 in 2041) and Vegetation growth (160.53 km2 in 2021 to 283.46 km2 in 2041), whereas Barren land and Water bodies are projected to shrink significantly. These findings reveal significant anthropogenic dominance over land cover alterations, where Agricultural expansion and Vegetation fluctuations are projected to drive future LULC changes, potentially threatening the biological richness of the Kalsubai–Harishchandragad Wildlife Sanctuary.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vinit Dhaigude
AU  - Prasad Balasaheb Wale
AU  - Thota Sivasankar
AU  - Swakangkha Ghosh
AU  - Sangeeta Sarmah
PY  - 2025
DA  - 2025/12/31
TI  - Artificial Neural Network - Cellular Automata (ANN–CA) Modelling for Spatio-Temporal LULC Dynamics in Semi-Arid Akole Tehsil, Maharashtra
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 147
EP  - 159
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_11
DO  - 10.2991/978-94-6463-940-7_11
ID  - Dhaigude2025
ER  -