2 research outputs found

    Deep learning-based multi-target regression for traffic-related air pollution forecasting

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    Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air quality data to gain valuable insights into its effect on public health. Over the years, the research community has developed advanced methods of forecasting traffic-related pollution using several machine learning methods albeit with persistent accuracy and insufficient data challenges. Despite the potentials of emerging techniques such as multi-target deep neural network to achieve optimal solutions, they are yet to be fully exploited in the air quality space due to their complexity and unavailability of the right training data. It is to this end that this study investigates the impact of integrating an updated data set including road elevation, vehicle emissions factor and background maps with traffic flow, weather and pollution data on TRAP forecasting. To explore the robustness and adaptability of our methodology, the study was carried out in one major city (London), one smaller city (Newport) and one large town (Chepstow) in the United Kingdom. The forecasting task was modelled as a multi-target regression problem and experiments were carried out to predict N O 2 , P M 2 . 5 and P M 10 concentrations over multiple timesteps. Fastai’s tabular model was used alongside prophet’s time-series model and scikit-learn’s multioutputregressor for experimentation with fastai recording the overall best performance. Statistical tests run using Friedman and Wilcoxon test also revealed the significance of the fastai model with a p-values < 0.05. Finally, a model explanation tool was then used to reveal the most and least influential features from the newly curated data set. Results showed traffic count and speed were part of the most contributing features. This result demonstrates the impact of these and other introduced features on TRAP forecasting and will serve as a foundation for related studies

    Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

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    Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications
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