When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria

2025-08-22

AI Summary

Air pollution forecasting in Northern Nigeria faces significant challenges due to sparse, irregular monitoring data and limited infrastructure. Method: This study comparatively evaluates Facebook Prophet and Long Short-Term Memory (LSTM) networks for monthly forecasting of CO, SO₂, and SO₄ concentrations, using observational data from 19 states spanning 2018–2023. Contribution/Results: Prophet achieves accuracy comparable to or exceeding that of LSTM across most pollutants—particularly under conditions of strong seasonality, long-term trends, and limited sample size. These findings challenge the prevailing assumption that higher model complexity inherently yields superior performance, underscoring instead the critical importance of aligning model selection with intrinsic data characteristics (e.g., sparsity, periodic structure). The study proposes Prophet as a lightweight, interpretable, and cost-effective alternative for environmental time-series forecasting in resource-constrained settings. It advances a methodology prioritizing model appropriateness over complexity, offering practical guidance for sustainable air quality monitoring in data-scarce regions.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Evaluating Prophet vs LSTM for air pollution forecasting
• Addressing data scarcity challenges in Northern Nigeria
• Comparing model performance under constrained data conditions

Innovation

• Prophet model matches LSTM accuracy
• Simpler methods outperform deep learning
• Context-sensitive efficient forecasting preferred