Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil
文献类型：期刊论文
| 作者 | Li, Zhichao |
| 刊名 | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 19期号:20页码:16 |
| 关键词 | dengue risk prediction big geospatial data Google Earth Engine cloud deep learning Google Colab |
| DOI | 10.3390/ijerph192013555 |
| 通讯作者 | Li, Zhichao(lizc@igsnrr.ac.cn) |
| 英文摘要 | Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (R-sum), mean temperature (T-mean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling. |
| WOS关键词 | BIG DATA APPLICATIONS |
| 资助项目 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDBSSW-DQC005] ; Strategic Priority Research Program of the CAS[XDA19040301] ; Institute of Geographic Sciences and Natural Resources Research (IGNSRR), Chinese Academy of Sciences (CAS)[E0V00110YZ] |
| WOS研究方向 | Environmental Sciences & Ecology ; Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:000873037700001 |
| 出版者 | MDPI |
| 资助机构 | Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS) ; Strategic Priority Research Program of the CAS ; Institute of Geographic Sciences and Natural Resources Research (IGNSRR), Chinese Academy of Sciences (CAS) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/186144]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Zhichao |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhichao. Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2022,19(20):16. |
| APA | Li, Zhichao.(2022).Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,19(20),16. |
| MLA | Li, Zhichao."Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 19.20(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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