A Data-Driven Environmental Ripple Simulation Model

Environmental sustainability is increasingly influenced by the cumulative impact of small-scale technological actions. This paper presents a machine learning-based framework to simulate environmental ripple effects by linking micro-level technological behaviors with macro-level ecological outcomes. The model integrates multimodal datasets, including satellite imagery, climate records, IoT sensor data, and behavioral signals. Various machine learning techniques, including logistic regression, LSTM networks, and transformer architectures, are evaluated. Experimental results demonstrate that transformer-based models achieve superior contextual understanding and predictive performance, enabling early detection of ecological risks. The proposed system supports scalable environmental monitoring, forecasting, and decision-making for sustainability. This work highlights the importance of AI-driven approaches in understanding and mitigating long-term environmental consequences of everyday technological activities.