Modeling Elephant Migration and Deforestation Hotspots in Sri Lanka's Dry Forests Using Hybrid CNN-LSTM Architectures and Cellular Automata

Abstract

Sri Lanka's dry forests, spanning approximately 15,500 km2, represent a biodiverse yet critically threatened ecosystem in South Asia, facing escalating pressures from climate change, agricultural expansion, and human-wildlife conflict. This study develops an integrated machine learning (ML) and remote sensing framework for spatiotemporal change detection (2015-2025) to address these challenges, leveraging Sentinel2 multispectral imagery. This study proposes a hybrid 3D-CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) model that integrates spectral-temporal feature fusion to achieve monsoon-resilient land cover classification, attaining 92.4% accuracy (K=0.89) -a 7.2% improvement over conventional SVM-PCA methods (85.2%,K=0.81). Key innovations include (1) a Genetic Algorithm-optimized Degradation Risk Index (GA-DRI) incorporating 12 ecological variables, and (2) Cellular Automata (CA) modeling of elephant migration under RCP4.5 climate scenarios. Results identify six deforestation hotspots (>5km2 each) in Anuradhapura District, strongly correlated with agricultural encroachment (r=0.78,p<0.01) and declining groundwater tables (r=−0.65). This framework supports Sri Lanka's National Adaptation Plan (2022-2030) and advances progress toward UN Sustainable Development Goal 15 (Life on Land) through actionable, high-resolution conservation metrics.