دورية أكاديمية
Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors
| العنوان: | Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors |
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| المؤلفون: | Peng-Yeng Yin, Chih-Chun Tsai, Rong-Fuh Day, Ching-Ying Tung, Bir Bhanu |
| المصدر: | Mathematical Biosciences and Engineering, Vol 16, Iss 6, Pp 6858-6873 (2019) |
| بيانات النشر: | AIMS Press, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Biotechnology LCC:Mathematics |
| مصطلحات موضوعية: | ensemble learning, low-cost sensors, air quality index, particle swarm optimization, pm2.5, spatiotemporal data, sensor calibratio, Biotechnology, TP248.13-248.65, Mathematics, QA1-939 |
| الوصف: | The PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1551-0018 |
| Relation: | https://www.aimspress.com/article/10.3934/mbe.2019343?viewType=HTML; https://doaj.org/toc/1551-0018 |
| DOI: | 10.3934/mbe.2019343?viewType=HTML |
| DOI: | 10.3934/mbe.2019343 |
| URL الوصول: | https://doaj.org/article/be9d2206b8354a91a8333ae2c4e557c3 |
| رقم الأكسشن: | edsdoj.be9d2206b8354a91a8333ae2c4e557c3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 15510018 |
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| DOI: | 10.3934/mbe.2019343?viewType=HTML |