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学者姓名：卢毅敏
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| GB/T 7714 | Yimin Lu . Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction [J]. | Mathematics , 2024 : 1-20 . |
| MLA | Yimin Lu . "Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction" . | Mathematics (2024) : 1-20 . |
| APA | Yimin Lu . Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction . | Mathematics , 2024 , 1-20 . |
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Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.
Keyword ：
COVID-19 COVID-19 ensemble empirical mode decomposition ensemble empirical mode decomposition ensemble prediction ensemble prediction fuzzy entropy fuzzy entropy LSTM network LSTM network
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| GB/T 7714 | Ke, Wenhui , Lu, Yimin . Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction [J]. | MATHEMATICS , 2024 , 12 (3) . |
| MLA | Ke, Wenhui 等. "Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction" . | MATHEMATICS 12 . 3 (2024) . |
| APA | Ke, Wenhui , Lu, Yimin . Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction . | MATHEMATICS , 2024 , 12 (3) . |
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基于2001—2021年Landsat遥感数据，研究巢湖流域植被覆盖多年变化，利用时空地理加权模型探讨地形、气候及人类活动的驱动影响。结果表明：(1)流域内平均植被覆盖度0.75，近20 a总体呈增加趋势，增速0.12%/a;2009年前，呈下降趋势，2009年后，呈增加趋势。(2)流域内89.79%的区域植被覆盖度高于0.6，不同土地利用的植被覆盖度为林地>旱作农田>稀疏植被>灌溉农田>城市建成区>湿地，湿地受蓄水排涝功能的影响，植被覆盖度相对较低。(3)近20 a,34.78%的区域植被覆盖度发生变化，以增加趋势为主，面积占比62.73%，集中在合肥市老城区、山区林地、沿江北岸平原地带以及环巢湖湿地区域，政策上的生态环境保护和流域治理对植被覆盖产生正影响成效显著；减少趋势发生在城市外围和部分耕地区域，体现城镇化建设、耕地种植模式等人类活动对植被覆盖的负影响，对于引江济淮（派河—东淝河）段施工沿线和柘皋河流域植被覆盖度减少，应值得关注。(4)2010年前，地形因素的影响逐年降低，气候作用逐年增加，2010年后，地形因素持续增加并成为主要因素。(5)人类活动对植被覆盖的影响程度整体呈增强趋势，生态环境得到改善，增强幅度为0.013%/（10 a）。
Keyword ：
Landsat遥感数据 Landsat遥感数据 巢湖流域 巢湖流域 时空分析 时空分析 时空地理加权模型 时空地理加权模型 植被覆盖度 植被覆盖度 驱动影响 驱动影响
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| GB/T 7714 | 王亚琼 , 高曼莉 , 罗劲松 et al. 2001—2021年巢湖流域植被覆盖时空变化及驱动分析 [J]. | 长江科学院院报 , 2024 , 41 (06) : 58-68 . |
| MLA | 王亚琼 et al. "2001—2021年巢湖流域植被覆盖时空变化及驱动分析" . | 长江科学院院报 41 . 06 (2024) : 58-68 . |
| APA | 王亚琼 , 高曼莉 , 罗劲松 , 徐莹梅 , 徐伟 , 卢毅敏 . 2001—2021年巢湖流域植被覆盖时空变化及驱动分析 . | 长江科学院院报 , 2024 , 41 (06) , 58-68 . |
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Aquaculture has experienced significant growth, contributing to resolving the global food crisis and delivering substantial economic benefits. Nevertheless, the uncontrolled expansion of aquaculture activities has led to an ecological crisis in offshore waters. This highlights the critical need for precise delineation and monitoring of aquaculture areas in these regions to ensure scientific management and sustainable development of coastal areas. In this article, we introduced an SRUNet model based on the Swin Transformer for accurately extracting offshore raft aquaculture areas using medium-resolution remote sensing images. Our SRUNet model combined the UNet model with the Swin Transformer block and the residual block to account for multiscale features, resulting in excellent extraction performance in diverse and complex sea areas. To evaluate the model, we selected four typical raft aquaculture areas and compared the SRUNet model with other comparative network models. Results revealed that the SRUNet model outperformed all other models, and the F1 Score and MIoU of the classification results were 86.52% and 87.22%, respectively. The model reduced the loss of feature information and misclassification of aquaculture areas, generating extraction effects that aligned closely with real aquaculture area shapes. Additionally, we tested the performance of each component of the SRUNet model. The results indicate that the SRUNet model exhibits strong robustness and effectively filters out irrelevant information. These results demonstrate the model's potential for large-scale extraction of offshore aquaculture areas.
Keyword ：
Aquaculture Aquaculture Biological system modeling Biological system modeling Feature extraction Feature extraction Optical imaging Optical imaging Optical reflection Optical reflection Optical sensors Optical sensors Raft aquaculture Raft aquaculture Remote sensing Remote sensing residual block residual block sentinel series satellites data sentinel series satellites data Swin Transformer Swin Transformer
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| GB/T 7714 | Liu, Jin , Lu, Yimin , Guo, Xiangzhong et al. A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 6296-6309 . |
| MLA | Liu, Jin et al. "A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 6296-6309 . |
| APA | Liu, Jin , Lu, Yimin , Guo, Xiangzhong , Ke, Wenhui . A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 6296-6309 . |
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Tea is one of the world's top three beverages, and the tea industry is an important pillar of China's agricultural economy. The sustainable development of the tea industry requires rapid and accurate tea plantations mapping. In this paper, a novel deep learning model (TeaNet) was proposed to extract tea plantations from medium-resolution remote sensing images. The TeaNet model, which was designed as a U-shaped network structure, improved performance by coupled Swin Transformer and convolutional neural network (CNN). Furthermore, the Sentinel 2A images of Wuyishan City were utilized to validate the proposed model. The results indicated that the TeaNet model shows a good performance, with a recall of 82.75%, and an F1 score of 79.48% which outperforms the UNet (improved of 26.58% and 14.99%). This indicates that the TeaNet model can significantly overcome the interference of irrelevant information and reduce the edge adhesion of tea plantations, thereby identifying the planting areas of tea plantations and providing an effective method for large-scale tea plantation mapping. © 2023 IEEE.
Keyword ：
Convolutional Neural Network Convolutional Neural Network deep learning deep learning remote sensing images remote sensing images Swin Transformer Swin Transformer tea plantations mapping tea plantations mapping
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| GB/T 7714 | Guo, X. , Liu, J. , Lu, Y. . Mapping Tea Plantations from Medium-Resolution Remote Sensing Images Using Convolutional Neural Networks and Swin Transformer [未知]. |
| MLA | Guo, X. et al. "Mapping Tea Plantations from Medium-Resolution Remote Sensing Images Using Convolutional Neural Networks and Swin Transformer" [未知]. |
| APA | Guo, X. , Liu, J. , Lu, Y. . Mapping Tea Plantations from Medium-Resolution Remote Sensing Images Using Convolutional Neural Networks and Swin Transformer [未知]. |
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本文采用XGBoost机器学习算法，融合臭氧浓度地面监测数据、欧洲中期天气预报中心的ERA5数据集、中国多尺度排放清单模型构建的排放清单数据集、高分辨率遥感影像（TROPOMI＿NO_2、OMI＿NO_2）以及人口数据和DEM数据，构建训练估算数据集，开展近地面臭氧浓度估算研究.模型构建采用递归式特征消除法进行特征变量的选择，并对其进行十折交叉和自建模验证，R~2分别为0.871和0.955,RMSE分别为12.8μg·m~(-3)和7.514μg·m~(-3).同时进行了高分辨率遥感影像对估算结果的贡献分析，结果表明引入TROPOMI＿NO_2因子参与建模可校正近地面臭氧浓度普遍被低估现象....
Keyword ：
OMI OMI TROPOMI TROPOMI XGBoost XGBoost 时空分布 时空分布 近地面臭氧 近地面臭氧
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| GB/T 7714 | 赵楠 , 卢毅敏 . 基于XGBoost算法的近地面臭氧浓度遥感估算 [J]. | 环境科学学报 , 2022 , 42 (05) : 95-108 . |
| MLA | 赵楠 et al. "基于XGBoost算法的近地面臭氧浓度遥感估算" . | 环境科学学报 42 . 05 (2022) : 95-108 . |
| APA | 赵楠 , 卢毅敏 . 基于XGBoost算法的近地面臭氧浓度遥感估算 . | 环境科学学报 , 2022 , 42 (05) , 95-108 . |
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Within the context of PM2.5 concentrations decreasing annually, ozone concentrations have increased instead of decreased, and ozone has become one of the main pollutants in the warm season in China. Based on the idea of big data association analysis, the extreme gradient boosting (XGBoost) ozone concentration estimation model was constructed and developed to estimate the maximum daily 8 h average ozone concentration (O3_8h) in China in 2019 for human exposure assessment. The model input ground monitoring station data, high-resolution remote-sensing satellite data, meteorological data, emission inventory data, digital elevation model (DEM) data, and population data were used to capture the temporal and spatial variation of O3_8h. In this study, ten-fold cross-validation was used to evaluate the estimation performance of the model (R2=0.871, RMSE=11.7 μg•m-3). Compared to those with the random forest (RF) model and kernel ridge regression (KRR) model, due to the improvement in the algorithm itself and the advancement of parallel processing, the estimation results of the XGBoost model showed higher accuracy (RF: R2=0.864, RMSE=12.387 μg•m-3). The KRR model was as follows: R2=0.582, RMSE=23.1 μg•m-3, and the computational efficiency of the model was significantly improved. At the same time, the level of ozone exposure and the relative risk of death due to chronic obstructive pulmonary disease (COPD) in China's provinces and cities were evaluated. The results showed that the top five number of days exceeding the standard occurred in Shandong Province, Henan Province, Hebei Province, Anhui Province, and the Ningxia Hui Autonomous Region. In terms of exposure intensity, Hebei Province, Shandong Province, Shanxi Province, Tianjin City, and Jiangsu Province ranked the top five in terms of population weighted ozone concentration. In terms of health effects, the relative risk of COPD death showed seasonal changes, with the highest in summer and the lowest in winter. © 2022, Science Press. All right reserved.
Keyword ：
Computational efficiency Computational efficiency Decision trees Decision trees Health risks Health risks Meteorology Meteorology Ozone Ozone Population statistics Population statistics Pulmonary diseases Pulmonary diseases Regression analysis Regression analysis Remote sensing Remote sensing
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| GB/T 7714 | Zhao, Nan , Lu, Yi-Min . Estimation of Surface Ozone Concentration and Health Impact Assessment in China [J]. | Environmental Science , 2022 , 43 (3) : 1235-1245 . |
| MLA | Zhao, Nan et al. "Estimation of Surface Ozone Concentration and Health Impact Assessment in China" . | Environmental Science 43 . 3 (2022) : 1235-1245 . |
| APA | Zhao, Nan , Lu, Yi-Min . Estimation of Surface Ozone Concentration and Health Impact Assessment in China . | Environmental Science , 2022 , 43 (3) , 1235-1245 . |
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在PM_(2.5)浓度逐年下降的背景下，臭氧浓度不降反升，臭氧已成为中国暖季的主要污染物之一.基于大数据关联分析思路，构建并开发了极限梯度提升(XGBoost)臭氧浓度估算模型，用以估算2019年中国每日最大8 h平均臭氧浓度(O_3_8h),用于人类暴露评估.该模型输入地面监测站点数据、高分辨率遥感卫星数据、气象数据、排放清单数据、数字高程模型(DEM)数据和人口数据，捕捉O_3_8h的时空变化.本研究采用十折交叉验证的方式评估模型的估算性能(R~2为0.871,RMSE为11.7μg·m~(-3)),与随机森林模型(RF)和核岭回归模型(KRR)相比，由于算法本身的提升和并行处理的推进，使...
Keyword ：
人口暴露 人口暴露 地表臭氧 地表臭氧 对流层观测仪(TROPOMI) 对流层观测仪(TROPOMI) 慢性阻塞性肺部疾病 慢性阻塞性肺部疾病 极限梯度提升算法(XGBoost) 极限梯度提升算法(XGBoost)
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| GB/T 7714 | 赵楠 , 卢毅敏 . 中国地表臭氧浓度估算及健康影响评估 [J]. | 环境科学 , 2022 , 43 (03) : 1235-1245 . |
| MLA | 赵楠 et al. "中国地表臭氧浓度估算及健康影响评估" . | 环境科学 43 . 03 (2022) : 1235-1245 . |
| APA | 赵楠 , 卢毅敏 . 中国地表臭氧浓度估算及健康影响评估 . | 环境科学 , 2022 , 43 (03) , 1235-1245 . |
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近年来,臭氧成为我国各大城市大气的首要污染物,因此对流层臭氧产品对于监测近地面臭氧浓度十分重要,然而现有的对流层臭氧产品不能满足高空间分辨率、高时间分辨率的监测要求.利用时空拟合法对臭氧监测仪(OMI)臭氧总量数据进行修复,再根据对流层臭氧残差法反演中国区域的对流层臭氧总量数据,其结果表明:从定性的角度考虑,时空拟合法具有更好的修复效果,从定量的角度考虑,时空拟合法相对于克里金插值法和反距离加权法的RMSE、MAE均较小.利用对流层臭氧残差法得到的对流层臭氧廓线数据与OMI/MLS的官方臭氧产品有着较高的相关性,其相关系数R最高为0.82.
Keyword ：
卫星遥感数据 卫星遥感数据 反演 反演 对流层臭氧 对流层臭氧 数据修复 数据修复 时空加权法 时空加权法 臭氧监测仪 臭氧监测仪
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| GB/T 7714 | 徐军 , 卢毅敏 . 一种基于OMI数据的中国区域对流层O_3的反演方法 [J]. | 南京信息工程大学学报(自然科学版) , 2021 , 13 (06) : 707-719 . |
| MLA | 徐军 et al. "一种基于OMI数据的中国区域对流层O_3的反演方法" . | 南京信息工程大学学报(自然科学版) 13 . 06 (2021) : 707-719 . |
| APA | 徐军 , 卢毅敏 . 一种基于OMI数据的中国区域对流层O_3的反演方法 . | 南京信息工程大学学报(自然科学版) , 2021 , 13 (06) , 707-719 . |
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With the rapid development of economy, the problem of river pollution is becoming more and more serious. It is very important and challenging to build a high-precision water quality prediction model for the comprehensive management of water environment and prevention of water pollution. At present, some achievements have been made in data-driven river water quality prediction, mainly including grey model, time series model, support vector machine model and neural network model. However, these existing prediction methods are limited to single station prediction. The influence of the upstream water quality on the downstream water quality is ignored and the upstream stations at different locations will have different effects on the downstream water quality. In order to solve the problem that the traditional model of water quality prediction does not consider the upstream influence and difficulties in long-term prediction, a new water quality prediction model based on spatiotemporal attention mechanism and long-short-term memory neural network (TS-Attention-LSTM) was proposed, which considers the spatiotemporal correlation and meteorological factors. Firstly, the spatial attention module was embedded in the encoder to extract the significant spatial correlation between upstream and downstream. The interaction among the water quality indicators was also extracted. Then, the temporal attention module was embedded in the decoder to extract the important time series features. Moreover, the meteorological factors and spatial characteristics were fused in the decoder. Finally, the multi-step prediction of water quality was carried out by using LSTM model. In this paper, the research area located in Jinjiang River Watershed in Fujian Province, China. The nonpoint source pollution in this river basin mainly interrelated with livestock discharge, agricultural field runoff and rural domestic sewage. The results show that the TS-Attention-LSTM model can effectively capture the spatial and temporal characteristics of water quality index (such as dissolved oxygen, DO and total phosphorus, TP) and the influence of meteorological factors in Jinjiang River Basin. The mean absolute error (MAE) of the TS-Attention-LSTM model was 0.24, the mean absolute percent error (MAPE) was 3.36%, and the root mean square error (RMSE) was 0.32, which performed best in all comparison models. The determinable coefficient R2 was 0.5062, performed second best in all comparison models. © 2021 IEEE.
Keyword ：
Agricultural robots Agricultural robots Agricultural runoff Agricultural runoff Agriculture Agriculture Biochemical oxygen demand Biochemical oxygen demand Decoding Decoding Dissolved oxygen Dissolved oxygen Errors Errors Long short-term memory Long short-term memory Mean square error Mean square error Predictive analytics Predictive analytics River pollution River pollution Rivers Rivers Sewage Sewage Support vector machines Support vector machines Time series Time series Water management Water management Water quality Water quality Watersheds Watersheds Weather forecasting Weather forecasting
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| GB/T 7714 | Lu, Yi-Min , Zhang, Hong , Shao, Wei . Prediction of river water quality considering spatiotemporal correlation and meteorological factors [C] . 2021 . |
| MLA | Lu, Yi-Min et al. "Prediction of river water quality considering spatiotemporal correlation and meteorological factors" . (2021) . |
| APA | Lu, Yi-Min , Zhang, Hong , Shao, Wei . Prediction of river water quality considering spatiotemporal correlation and meteorological factors . (2021) . |
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