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Integration of Multi-modal Remote Sensing Data and LSTM Techniques for Gaseous Pollutants Estimation in Tehran | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 9، شماره 2، اسفند 2025 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2025.398306.1180 | ||
| نویسندگان | ||
| Mina Saleh1؛ Reza Shah-Hosseini* 2؛ Armin Moghimi3 | ||
| 1School of Mapping and Spatial Information Engineering, Non-Profit University of Pouyandegan-e Danesh (Chaloos), Mazandaran, Iran | ||
| 2University of Tehran | ||
| 3Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran | ||
| چکیده | ||
| Gaseous pollutants present growing challenges in major urban centers, posing serious threats to public health and the environment. In cities such as Tehran, where pollution levels are critically high, nitrogen dioxide (NO₂) and carbon monoxide (CO) play significant in environmental harm and health concerns. Conventional prediction models frequently depend on single-source datasets, overlooking the advantages that multi-source Remote Sensing (RS) data can offer in improving the accuracy of forecasts. To address this issue, We propose a novel approach using Long Short-Term Memory (LSTM) neural networks to estimate gaseous pollutant concentrations by incorporating multi-source RS data. This approach utilizes input from MODIS sensors (surface temperatures, aerosol optical depth, thermal emissivity) along with ground-based particulate matter data (PM₁₀, PM₂.₅). Using a five-year dataset (January 2018–January 2024) from Tehran's air quality stations, we allocated 70% of the data for training and 30% for testing and validation. Our results reveal that the LSTM-based model outperformed existing methods like Gated Recurrent Unit (GRU), Random Forest (RF), One-Dimensional Convolutional Neural Network (1D-CNN) in predicting pollutant concentrations, with greater accuracy and reliability for forecasting. For CO, the R² values were 0.531 (GRU), 0.390 (1D-CNN), 0.498 (RF), 0.522 (Transformer), and 0.566 (LSTM); for NO₂, they were 0.953, 0.952, 0.907, 0.864 and 0.957, respectively. These results validate the superior performance of LSTM in capturing complex non-linear relationships in multi-source RS data for our case study. The improved model performance is beneficial for urban planning, pollution mitigation strategies, and public health protection. | ||
| کلیدواژهها | ||
| Air pollution؛ Long Short-Term Memory (LSTM)؛ gaseous pollutants and MODIS sensor | ||
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آمار تعداد مشاهده مقاله: 80 |
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