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Zhang X, Yang L, Chen T, Wang Q, Yang J, Zhang T, Yang J, Zhao H, Lai S, Feng L, Yang W. Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study 1. Infect Dis Model 2024; 9:816-827. [PMID: 38725432 PMCID: PMC11079460 DOI: 10.1016/j.idm.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
Background Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.
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Affiliation(s)
- Xingxing Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, China
| | - Liuyang Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, 650506, China
| | - Teng Chen
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, 11794-3600, USA
| | - Qing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
| | - Jin Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
| | - Jiao Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
| | - Hongqing Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100073, China
- State Key Laboratory of Respiratory Health and Multimorbidity, China
- Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College), Ministry of Education, China
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Huang J, Chen J, Mu Y, Cao C, Shen H. Remote-sensing monitoring of colored dissolved organic matter in the Arctic Ocean. MARINE POLLUTION BULLETIN 2024; 204:116529. [PMID: 38824705 DOI: 10.1016/j.marpolbul.2024.116529] [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: 03/23/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/04/2024]
Abstract
In the Arctic Ocean, variations in the colored dissolved organic matter (CDOM) have important value and significance. This study proposed and evaluated a novel method by combining the Google Earth Engine with a multilayer back-propagation neural network to retrieve CDOM concentration. This model performed well on the testing data and independent validation data (R2 = 0.76, RMSE = 0.37 m-1, MAPD = 35.43 %), and it was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images. The CDOM distribution in the Arctic Ocean and its main sea areas was first depicted during the ice-free period from 2002 to 2021, with average CDOM concentration in the range of 0.25 and 0.31 m-1. High CDOM concentration appeared in coastal areas affected by rivers on the Siberian side. The CDOM concentration was highly correlated with salinity (r = -0.92) and discharge (r > 0.68), while melting sea ice diluted seawater and CDOM concentration.
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Affiliation(s)
- Jue Huang
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Junjie Chen
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Yulei Mu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Chang Cao
- College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Huagang Shen
- Qingdao Topscomm Communication Co., Ltd, Qingdao 266109, China
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Zhang D, Shi K, Wang W, Wang X, Zhang Y, Qin B, Zhu M, Dong B, Zhang Y. An optical mechanism-based deep learning approach for deriving water trophic state of China's lakes from Landsat images. WATER RESEARCH 2024; 252:121181. [PMID: 38301525 DOI: 10.1016/j.watres.2024.121181] [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: 08/16/2023] [Revised: 12/21/2023] [Accepted: 01/22/2024] [Indexed: 02/03/2024]
Abstract
Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally important to monitor and assess water trophic status of lakes. However, a reliable model for accurately estimating the trophic state index (TSI) of lakes across a large-scale region is still lacking due to their high complexity. Here, we proposed an optical mechanism-based deep learning approach to remotely estimate TSI of lakes based on Landsat images. The approach consists of two steps: (1) determining the optical indicators of TSI and modeling the relationship between them, and (2) developing an approach for remotely deriving the determined optical indicator from Landsat images. With a large number of in situ datasets measured from lakes (2804 samples from 88 lakes) across China with various optical properties, we trained and validated three machine learning methods including deep neural network (DNN), k-nearest neighbors (KNN) and random forest (RF) to model TSI with the optical indicators and TSI and derive the determined optical indicator from Landsat images. The results showed that (1) the total absorption coefficients of optically active constituents at 440 nm (at-w(440)) performs best in characterizing TSI, and (2) DNN outperforms other models in the inversion of both TSI and at-w(440). Overall, our proposed optical mechanism-based deep learning approach demonstrated a robust and satisfactory performance in assessing TSI using Landsat images (root mean squared error (RMSE) = 5.95, mean absolute error (MAE) = 4.81). This highlights its merit as a nationally-adopted method in lake water TSI estimation, enabling the convenience of the acquisition of water eutrophic information in large scale, thereby assisting us in managing lake ecology. Therefore, we assessed water TSI of 961 lakes (>10 km2) across China using the proposed approach. The resulting at-w(440) and TSI ranged from 0.01 m-1 to 31.42 m-1 and from 6 to 96, respectively. Of all these studied lakes, 96 lakes (11.40 %) were oligotrophic, 338 lakes were mesotrophic (40.14 %), 360 lakes were eutrophic (42.76 %), and 48 were hypertrophic (5.70 %) in 2020.
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Affiliation(s)
- Dong Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kun Shi
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China.
| | - Weijia Wang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Xiwen Wang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Yunlin Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Boqiang Qin
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Mengyuan Zhu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Baili Dong
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
| | - Yibo Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China
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Zhi W, Appling AP, Golden HE, Podgorski J, Li L. Deep learning for water quality. NATURE WATER 2024; 2:228-241. [PMID: 38846520 PMCID: PMC11151732 DOI: 10.1038/s44221-024-00202-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 01/10/2024] [Indexed: 06/09/2024]
Abstract
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
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Affiliation(s)
- Wei Zhi
- The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
| | | | - Heather E Golden
- Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH, USA
| | - Joel Podgorski
- Department of Water Resources and Drinking Water, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dübendorf, Switzerland
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
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Zhao D, Huang J, Li Z, Yu G, Shen H. Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169152. [PMID: 38061660 DOI: 10.1016/j.scitotenv.2023.169152] [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: 07/14/2023] [Revised: 11/11/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.
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Affiliation(s)
- Desong Zhao
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Jue Huang
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Zhengmao Li
- Shandong Marine Resource and Environment Research Institute, Shandong Key Laboratory of Marine Ecological Restoration, Yantai 264006, China
| | - Guangyue Yu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Huagang Shen
- Qingdao Topscomm Communication Co., Ltd, TOPSCOMM Industry Park, Qingdao 266109, China
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Fang C, Song C, Wen Z, Liu G, Wang X, Li S, Shang Y, Tao H, Lyu L, Song K. A novel chlorophyll-a retrieval model based on suspended particulate matter classification and different machine learning. ENVIRONMENTAL RESEARCH 2024; 240:117430. [PMID: 37866530 DOI: 10.1016/j.envres.2023.117430] [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: 09/03/2023] [Revised: 10/05/2023] [Accepted: 10/15/2023] [Indexed: 10/24/2023]
Abstract
Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R2 from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R2, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.
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Affiliation(s)
- Chong Fang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Changchun Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China
| | - Zhidan Wen
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Ge Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Xiaodi Wang
- School of Geography and Tourism, Harbin University, Harbin, 150086, China
| | - Sijia Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Yingxin Shang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lili Lyu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
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Fang C, Song C, Wang X, Wang Q, Tao H, Wang X, Ma Y, Song K. A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167522. [PMID: 37793448 DOI: 10.1016/j.scitotenv.2023.167522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/06/2023]
Abstract
Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.
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Affiliation(s)
- Chong Fang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Changchun Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Xiangyu Wang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Qiang Wang
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Tao
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China
| | - Xiaodi Wang
- School of Geography and Tourism, Harbin University, Harbin 150086, China
| | - Yue Ma
- Jilin Jianzhu University, Changchun, China
| | - Kaishan Song
- Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng 252000, China.
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Wang J, Chen X. A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167631. [PMID: 37806589 DOI: 10.1016/j.scitotenv.2023.167631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/16/2023] [Accepted: 10/05/2023] [Indexed: 10/10/2023]
Abstract
Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.
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Affiliation(s)
- Jialin Wang
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Xiaoling Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
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Xiang J, Cui T, Li X, Zhang Q, Mu B, Liu R, Zhao W. Evaluating the effectiveness of coastal environmental management policies in China: The case of Bohai Sea. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 338:117812. [PMID: 36996563 DOI: 10.1016/j.jenvman.2023.117812] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
With marine pollution issues becoming serious and widespread, a series of coastal environmental managemental policies are being carried out worldwide, the effectiveness of which requires comprehensive evaluation. Taking the Bohai Sea (BS) of China as an example, which has been plagued by serious ecological and environmental issues for decades due to terrestrial pollution discharge, this study explored and quantified, for the first time to our best knowledge, the variability of water quality after initiating a dedicated 3-year pollution control action (Uphill Battle for Integrated Bohai Sea Management, UBIBM, 2018-2020) implemented by China's central government, with two water quality indexes of water color (Forel-Ule index, FUI) and transparency (Secchi disk depth, ZSD, m) from satellite observations. During the UBIBM, a significant improvement in water quality was detected, characterized by a clearer and bluer BS, with ZSD and FUI improved by 14.1% and 3.2%, respectively, compared with the baseline period (2011-2017). In addition, an abrupt drop in the long-term record (2011-2022) of the coverage area of highly turbid waters (ZSD≤2 m or FUI≥8) was found in 2018, which coincided with the start of the UBIBM, indicating that the water quality improvement may be attributed to the pollution alleviation of the UBIBM. Independent data of land-based pollution statistics also supported this deduction. (3) Compared with the previous two pollution control actions in the first decade of 21st century, UBIBM was proved to be the most successful one in terms of the achieved highest transparency and lowest FUI during the past two decades. Reasons for the achievement and implications to future pollution control are discussed for a more sustainable and balanced improvement in the coastal environment. This research provides a valuable example that satellite remote sensing can play a vital role in the management of coastal ecosystems by providing effective evaluation of pollution control actions.
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Affiliation(s)
- Jinzhao Xiang
- School of Atmospheric Sciences, Sun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
| | - Tingwei Cui
- School of Atmospheric Sciences, Sun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
| | - Xuyan Li
- School of Atmospheric Sciences, Sun Yat-Sen University & Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
| | - Qian Zhang
- University of Maryland Center for Environmental Science, Chesapeake Bay Program, 1750 Forest Drive, Suite 130, Annapolis, MD, 21401, USA
| | - Bing Mu
- Ocean University of China, Qingdao, 266071, China
| | - Rongjie Liu
- First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China
| | - Wenjing Zhao
- South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou, 510000, China
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10
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Yu D, Yang L, Li Y, Xiang J, Zhao C. Spatiotemporal changes and influencing factors of water clarity in the Yellow Sea over the past 20 years. MARINE POLLUTION BULLETIN 2023; 191:114904. [PMID: 37087829 DOI: 10.1016/j.marpolbul.2023.114904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 03/29/2023] [Accepted: 04/01/2023] [Indexed: 05/03/2023]
Abstract
Water transparency is an important parameter for describing the optical properties of water. It reflects changes in marine environment and is of great significance for guiding the development and protection of marine environment. In this study, based on the algorithm proposed by Lee et al. in 2015, water transparency in the Yellow Sea from 2003 to 2022 was inverted. The results revealed that, in terms of spatial distribution, the water in western region of the Yellow Sea had relatively low transparency, whereas the water in the central and southern regions had high transparency. Regarding temporal trends, declining transparency was observed throughout most of the study period, but the trends reversed and transparency began to increase in 2017. Empirical orthogonal function analysis confirmed that water transparency was primarily influenced by the optical constituents of water. Long-term monitoring of water clarity is of significant importance for the preservation of marine ecological environments.
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Affiliation(s)
- Dingfeng Yu
- School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China; Institute of Oceanograhic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China
| | - Lei Yang
- Institute of Oceanograhic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China
| | - Yunzhou Li
- School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China; Institute of Oceanograhic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Laoshan laboratory, Qingdao 266237, China.
| | - Jie Xiang
- School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.
| | - Chunyan Zhao
- Institute of Oceanograhic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China
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11
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Alshari EA, Abdulkareem MB, Gawali BW. Classification of land use/land cover using artificial intelligence (ANN-RF). Front Artif Intell 2023; 5:964279. [PMID: 36686849 PMCID: PMC9853425 DOI: 10.3389/frai.2022.964279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/18/2022] [Indexed: 01/08/2023] Open
Abstract
Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).
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Affiliation(s)
- Eman A. Alshari
- Department of Computer Science and Information Technology, Thamar University, Dhamar, Yemen,Department of Computer Engineering Techniques, Al-Maarif University College, Ramadi, Iraq,*Correspondence: Eman A. Alshari
| | | | - Bharti W. Gawali
- Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
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12
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Zhang Q, Ma X. How does lake water clarity affect lake thermal processes? Heliyon 2022; 8:e10359. [PMID: 36061021 PMCID: PMC9433690 DOI: 10.1016/j.heliyon.2022.e10359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/02/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to determine the effects of water clarity changes on thermal processes in Lake Poyang, the largest freshwater lake in China, using a physically based lake model embedded in the Community Land Model. A water extinction coefficient (Kd) describing water clarity and controlling radiation penetration in the lake model was used to conduct controlled simulations. Three sets of simulations were conducted for Lake Poyang over the period from 2000 to 2015: DEFAULT with the Kd = 0.45 m−1; CTL with the Kd = 1.68 m−1 based on a water clarity of 0.85 m; and DARK with the Kd = 1.68 m−1 from 2000 to 2005 and Kd = 3.44 m−1 based on a water clarity of 0.41 m observed from 2005 to 2015. The simulation results showed that compared with the DEFAULT simulation, the temperature simulations were closer to the observations using the more accurate Kd values for the CTL and DARK simulations. Due to decreased water clarity, radiation absorbed in the top 1 m of the water body was larger for the DARK simulation and lower at greater depths than that observed for the CTL simulation. Such changes in radiation penetration in the DARK simulation generated a higher lake water surface temperature (LWST) and thus stronger lake-air interactions from February to July and lower LWST and turbulent fluxes from August to the following January than in the CTL simulation. The temperature inside the lake water body declined markedly, with a significant reduction from June to August that exceeded 5 °C. The results of this study provide an additional reference regarding lake water clarity effects on inland freshwater systems and theoretical support for lake water system management.
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Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network. REMOTE SENSING 2022. [DOI: 10.3390/rs14133107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.
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