PM10 Concentration Forecasting Comparative Evaluation is a B.Tech project topic for Environmental Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
PM10 Concentration Forecasting Comparative Evaluation Project Details
| Abstract |
This project involves a comprehensive comparative evaluation of various forecasting methodologies for predicting PM10 concentrations, a critical indicator of urban air quality due to its association with respiratory and cardiovascular health issues. The study utilizes a dataset comprising three years of daily PM10 concentration records from the KadΔ±kΓΆy District of Istanbul, spanning from 2022 to 2024. The methodologies under investigation include classical time series models, specifically Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA), alongside advanced deep learning architectures such as Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks. All models are implemented and tested under standardized conditions to ensure a fair comparison.
Performance assessment is conducted using a suite of established metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (RΒ²), Mean Absolute Percentage Error (MAPE), and Mean Absolute Relative Error (MARE). The research aims to identify the most effective modeling approach for PM10 forecasting, acknowledging the inherent challenges in predicting daily concentrations solely based on historical data. Preliminary findings indicate that deep learning models, particularly GRU and LSTM, demonstrate superior performance with lower error values compared to traditional time series models.
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| Reference Paper |
PM10 Concentration Forecasting: A Comparative Evaluation of Deep Learning and Time Series Methods |
| Domain |
Environmental Engineering |
| Sub-Domain |
Air Pollution Monitoring |
| PDF Download |
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