کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4945161 | 1438298 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Forecasting smog-related health hazard based on social media and physical sensor
ترجمه فارسی عنوان
پیش بینی آسیب سلامت مربوط به دودمان بر اساس رسانه های اجتماعی و حسگر فیزیکی
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کلمات کلیدی
فاجعه سگ، خطر سلامت، رسانه های اجتماعی، داده های شهری، پیش بینی، داده کاوی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Smog disasters are becoming more and more frequent and may cause severe consequences on the environment and public health, especially in urban areas. Social media as a real-time urban data source has become an increasingly effective channel to observe people׳s reactions on smog-related health hazard. It can be used to capture possible smog-related public health disasters in its early stage. We then propose a predictive analytic approach that utilizes both social media and physical sensor data to forecast the next day smog-related health hazard. First, we model smog-related health hazards and smog severity through mining raw microblogging text and network information diffusion data. Second, we developed an artificial neural network (ANN)-based model to forecast smog-related health hazard with the current health hazard and smog severity observations. We evaluate the performance of the approach with other alternative machine learning methods. To the best of our knowledge, we are the first to integrate social media and physical sensor data for smog-related health hazard forecasting. The empirical findings can help researchers to better understand the non-linear relationships between the current smog observations and the next day health hazard. In addition, this forecasting approach can provide decision support for smog-related health hazard management through functions like early warning.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Systems - Volume 64, March 2017, Pages 281-291
Journal: Information Systems - Volume 64, March 2017, Pages 281-291
نویسندگان
Jiaoyan Chen, Huajun Chen, Zhaohui Wu, Daning Hu, Jeff Z. Pan,