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Huang H, Zhai M, Lei X, Chai B, Liao W, He L, Zuo X, Wang H. Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization. ENVIRONMENTAL RESEARCH 2023; 235:116606. [PMID: 37429396 DOI: 10.1016/j.envres.2023.116606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/30/2023] [Accepted: 07/08/2023] [Indexed: 07/12/2023]
Abstract
The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.
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Affiliation(s)
- Haocheng Huang
- School of Civil Engineering, Central South University, Changsha, 100038, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 410075, China
| | - Mingshuo Zhai
- Collaborative Innovation Center for Intelligent Regulation & Comprehensive Management of Water Resources, College of Water Resources and Hydropower, Hebei University of Engineering, Handan, 056038, China; Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China
| | - Xiaohui Lei
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 410075, China; Collaborative Innovation Center for Intelligent Regulation & Comprehensive Management of Water Resources, College of Water Resources and Hydropower, Hebei University of Engineering, Handan, 056038, China; Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China
| | - Beibei Chai
- Collaborative Innovation Center for Intelligent Regulation & Comprehensive Management of Water Resources, College of Water Resources and Hydropower, Hebei University of Engineering, Handan, 056038, China; Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China.
| | - Weihong Liao
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 410075, China
| | - Lixin He
- Collaborative Innovation Center for Intelligent Regulation & Comprehensive Management of Water Resources, College of Water Resources and Hydropower, Hebei University of Engineering, Handan, 056038, China; Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China
| | - Xiangyang Zuo
- Collaborative Innovation Center for Intelligent Regulation & Comprehensive Management of Water Resources, College of Water Resources and Hydropower, Hebei University of Engineering, Handan, 056038, China; Hebei Key Laboratory of Intelligent Water Conservancy, College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, China
| | - Hao Wang
- School of Civil Engineering, Central South University, Changsha, 100038, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 410075, China
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A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4718157. [PMID: 36277006 PMCID: PMC9581652 DOI: 10.1155/2022/4718157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 07/03/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022]
Abstract
The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.
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Qin T, Hao Y, Wu Y, Chen X, Zhang S, Wang M, Xiong W, He J. Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis. ENVIRONMENTAL RESEARCH 2022; 212:113279. [PMID: 35561834 DOI: 10.1016/j.envres.2022.113279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in "high and extremely high" HDI areas and subtropical areas than that in other subgroups (I2 = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.
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Affiliation(s)
- Tianyu Qin
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yu Hao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - You Wu
- Key Laboratory of Health Cultivation of the Ministry of Education, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xinli Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Shuwen Zhang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Mengqi Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Weifeng Xiong
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Juan He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
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