Predicting long-term environmental acoustic urban patterns using 2-slot short-term measurement and feed-forward artificial neural networks

Monitoring acoustic environments in urban ecosystems poses a major challenge due to the temporal and spatial variability of soundscapes. Long-term data collection, often extending over a year, is recommended by regulations to establish reliable acoustic profiles, but such efforts are resource-intensive. In this study, we introduce a computational ecology approach to predict long-term acoustic patterns in cities using optimized combinations of time intervals as input for artificial neural networks. Unlike conventional methods relying on a single temporal window, our framework evaluates paired time intervals to enhance predictive performance and capture the dynamics of complex urban soundscapes. Multiple neural network architectures were designed and comparatively assessed, demonstrating that 2-slot datasets consistently improved classification accuracy and Balanced Accuracy Micro-Averaging across all categories. On average, temporal pairing increased Balanced Accuracy from 0.576 to 0.763 in the most variable category, reflecting a 32.4% improvement. These results highlight the importance of temporal diversity in ecological data modelling and underscore the potential of computational techniques to optimize temporary monitoring stations. The proposed method supports more efficient, data-driven strategies for environmental noise prediction, with direct implications for sustainable urban ecosystem management and decision-making in the context of global environmental change. • 2-slot intervals improve long-term urban acoustic prediction over 1-slot methods. • Stability and robustness are improved in patterns with high variability. • The three-layer ANN excelled, with N e t _ 32 _ 32 _ 32 _ 4 achieving the highest accuracy. • The optimum 2-slot datasets include one nighttime and early-morning period.