When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
It addresses forecasting challenges for policymakers and practitioners in low-resource regions, though it is incremental as it compares existing methods on new data.
This study tackled air pollution forecasting in data-constrained Northern Nigeria by comparing Facebook Prophet and LSTM models, finding that Prophet often matches or exceeds LSTM's accuracy for seasonal trends, while LSTM is better for abrupt changes.
Air pollution forecasting is critical for proactive environmental management, yet data irregularities and scarcity remain major challenges in low-resource regions. Northern Nigeria faces high levels of air pollutants, but few studies have systematically compared the performance of advanced machine learning models under such constraints. This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants (CO, SO2, SO4) using monthly observational data from 2018 to 2023 across 19 states. Results show that Prophet often matches or exceeds LSTM's accuracy, particularly in series dominated by seasonal and long-term trends, while LSTM performs better in datasets with abrupt structural changes. These findings challenge the assumption that deep learning models inherently outperform simpler approaches, highlighting the importance of model-data alignment. For policymakers and practitioners in resource-constrained settings, this work supports adopting context-sensitive, computationally efficient forecasting methods over complexity for its own sake.