This study utilizes the XGBoost algorithm in the field of machine learning to conduct quantitative stock picking research for CSI 300 stocks. The article firstly outlines the importance and practical application background of quantitative stock selection, and then discusses in depth the basic principle of XGBoost algorithm and its application method in quantitative stock selection. By collecting historical data of CSI 300 stocks and after data preprocessing, this study constructs a multi-factor stock prediction model based on XGBoost and conducts relevant backtesting. Comparative experiments show that the XGBoost algorithm exhibits good effectiveness and demonstrates the unique advantages and characteristics of its stock selection strategy. The conclusion of the study shows that the XGBoost-based stock selection strategy has potential application value in the stock market and can provide investors with accurate and efficient stock selection reference.
{"title":"An Example of Machine Learning-Based Multifactor Dynamic Quantitative Stock Picking Models","authors":"Haocheng Sun","doi":"10.61173/7n8qt956","DOIUrl":"https://doi.org/10.61173/7n8qt956","url":null,"abstract":"This study utilizes the XGBoost algorithm in the field of machine learning to conduct quantitative stock picking research for CSI 300 stocks. The article firstly outlines the importance and practical application background of quantitative stock selection, and then discusses in depth the basic principle of XGBoost algorithm and its application method in quantitative stock selection. By collecting historical data of CSI 300 stocks and after data preprocessing, this study constructs a multi-factor stock prediction model based on XGBoost and conducts relevant backtesting. Comparative experiments show that the XGBoost algorithm exhibits good effectiveness and demonstrates the unique advantages and characteristics of its stock selection strategy. The conclusion of the study shows that the XGBoost-based stock selection strategy has potential application value in the stock market and can provide investors with accurate and efficient stock selection reference.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"317 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment analysis of film reviews has been a popular research topic, and previous researchers have investigated it on the IMDb dataset using a variety of machine learning models, however, the classification results are not satisfactory. Therefore this study aims to construct an effective sentiment analysis model and explore whether the Random Forest algorithm can be applied to the task of sentiment analysis on the IMDb dataset. In this study, after preprocessing the data, the Random Forest model was trained using a training set and evaluated using a test set to explore the accuracy and performance of the Random Forest model in film review sentiment analysis. The study also plotted word clouds to visualize the decision-making effect of the model. The Random Forest Model achieves an impressive 86% accuracy in sentiment analysis, while the word cloud plots provide a visually appealing depiction of its classification task. This indicates that the Random Forest model performs well in the film review sentiment analysis task with high accuracy and performance.
{"title":"Sentiment Analysis for Film Reviews Based on Random Forest","authors":"Dongling Zheng","doi":"10.61173/5t8epb44","DOIUrl":"https://doi.org/10.61173/5t8epb44","url":null,"abstract":"Sentiment analysis of film reviews has been a popular research topic, and previous researchers have investigated it on the IMDb dataset using a variety of machine learning models, however, the classification results are not satisfactory. Therefore this study aims to construct an effective sentiment analysis model and explore whether the Random Forest algorithm can be applied to the task of sentiment analysis on the IMDb dataset. In this study, after preprocessing the data, the Random Forest model was trained using a training set and evaluated using a test set to explore the accuracy and performance of the Random Forest model in film review sentiment analysis. The study also plotted word clouds to visualize the decision-making effect of the model. The Random Forest Model achieves an impressive 86% accuracy in sentiment analysis, while the word cloud plots provide a visually appealing depiction of its classification task. This indicates that the Random Forest model performs well in the film review sentiment analysis task with high accuracy and performance.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study addresses the predictive challenges in China’s A-share market, characterized by high retail investor participation and significant policy impacts. We introduce a novel predictive model that leverages both machine learning algorithms and sentiment analysis to forecast market trends. The research utilizes comprehensive datasets, including real-time A-share market data and sentiment-derived data from stock-related news, processed via advanced machine learning techniques like Random Forest and sentiment analysis tools. Our approach innovatively combines traditional technical indicators with sentiment scores to enhance the predictive accuracy of the model. The findings suggest that integrating sentiment analysis significantly improves the model’s performance, evidenced by enhanced prediction metrics such as Mean Absolute Error (MAE) and R-squared values, which compare favorably before and after incorporating sentiment data. This study not only contributes to the existing financial prediction literature by providing a hybrid methodological approach but also offers practical implications for investors and policymakers in navigating the volatile A-share market.
本研究探讨了中国 A 股市场的预测难题，该市场的特点是散户投资者参与度高且受政策影响较大。我们引入了一个新颖的预测模型，利用机器学习算法和情感分析来预测市场趋势。研究利用综合数据集，包括实时 A 股市场数据和股票相关新闻的情绪衍生数据，并通过随机森林等先进的机器学习技术和情绪分析工具进行处理。我们的方法创新性地将传统技术指标与情感评分相结合，以提高模型的预测准确性。研究结果表明，整合情感分析可显著提高模型的性能，这体现在平均绝对误差（MAE）和 R 平方值等预测指标的增强上，在整合情感数据之前和之后，这些指标的对比结果都很好。这项研究提供了一种混合方法，不仅为现有的金融预测文献做出了贡献，还为投资者和政策制定者驾驭动荡的 A 股市场提供了实际意义。
{"title":"A-share Trend Prediction Based on Machine Learning and Sentiment Analysis","authors":"Jiaming Zhang","doi":"10.61173/52jy4c52","DOIUrl":"https://doi.org/10.61173/52jy4c52","url":null,"abstract":"This study addresses the predictive challenges in China’s A-share market, characterized by high retail investor participation and significant policy impacts. We introduce a novel predictive model that leverages both machine learning algorithms and sentiment analysis to forecast market trends. The research utilizes comprehensive datasets, including real-time A-share market data and sentiment-derived data from stock-related news, processed via advanced machine learning techniques like Random Forest and sentiment analysis tools. Our approach innovatively combines traditional technical indicators with sentiment scores to enhance the predictive accuracy of the model. The findings suggest that integrating sentiment analysis significantly improves the model’s performance, evidenced by enhanced prediction metrics such as Mean Absolute Error (MAE) and R-squared values, which compare favorably before and after incorporating sentiment data. This study not only contributes to the existing financial prediction literature by providing a hybrid methodological approach but also offers practical implications for investors and policymakers in navigating the volatile A-share market.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"143 5‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the obstacle avoidance technology of unmanned aerial vehicles (UAVs). Firstly, the background, purpose, and significance of UAV obstacle avoidance technology are introduced. Then, the domestic and foreign research status is analyzed. Next, an overall plan for UAV obstacle avoidance is proposed, including demand analysis and system design. Afterwards, the theory and specific steps of binocular stereo vision obstacle positioning are discussed in detail, including camera calibration, image rectification, and stereo matching. Furthermore, methods for estimating obstacle motion states are researched. In addition, path planning methods for obstacle avoidance are explored, with a focus on the principles, issues, and improvement methods of artificial potential field method. Finally, the main achievements of this study are summarized, and future research directions are outlined. The innovation of this article lies in the proposal of an improved artificial potential field method, and the precise obstacle positioning achieved through binocular stereo vision. Future research can further optimize the path planning methods for obstacle avoidance to enhance the effectiveness and reliability of UAV obstacle avoidance.
{"title":"UAV Obstacle Avoidance Technology","authors":"Hongyi Wang","doi":"10.61173/vpv1ca75","DOIUrl":"https://doi.org/10.61173/vpv1ca75","url":null,"abstract":"This article investigates the obstacle avoidance technology of unmanned aerial vehicles (UAVs). Firstly, the background, purpose, and significance of UAV obstacle avoidance technology are introduced. Then, the domestic and foreign research status is analyzed. Next, an overall plan for UAV obstacle avoidance is proposed, including demand analysis and system design. Afterwards, the theory and specific steps of binocular stereo vision obstacle positioning are discussed in detail, including camera calibration, image rectification, and stereo matching. Furthermore, methods for estimating obstacle motion states are researched. In addition, path planning methods for obstacle avoidance are explored, with a focus on the principles, issues, and improvement methods of artificial potential field method. Finally, the main achievements of this study are summarized, and future research directions are outlined. The innovation of this article lies in the proposal of an improved artificial potential field method, and the precise obstacle positioning achieved through binocular stereo vision. Future research can further optimize the path planning methods for obstacle avoidance to enhance the effectiveness and reliability of UAV obstacle avoidance.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"102 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pneumonia is a serious disease that poses a threat to people’s health of all ages. It could happen when people are infected by viruses, fungi, bacteria etc. Typically, Chest X-rays are the first and foremost imaging approach to implement pneumonia detection. This paper introduces the latest research achievements to help those who are new in this field to have basic intuition about AI in pneumonia detection., including Vision Transformers on chest X-ray, a novel model based on RetinaNet, CP_DeepNet, and a novel Efficient NetV2L model. The last part gives some suggestions about the future study of pneumonia detection using Deep learning.
肺炎是一种严重的疾病，对所有年龄段的人的健康都构成威胁。当人们受到病毒、真菌、细菌等感染时，就有可能患上肺炎。通常情况下，胸部 X 光是检测肺炎的首要成像方法。本文介绍了最新的研究成果，以帮助初涉此领域的人员对人工智能在肺炎检测中的应用有基本的直观认识，包括胸部 X 射线上的视觉变换器、基于 RetinaNet 的新型模型、CP_DeepNet 和新型 Efficient NetV2L 模型。最后一部分对未来利用深度学习进行肺炎检测的研究提出了一些建议。
{"title":"A literature review of pneumonia detection algorithms based on deep learning and chest X-ray imaging","authors":"Chenyu Wang","doi":"10.61173/wpf17b07","DOIUrl":"https://doi.org/10.61173/wpf17b07","url":null,"abstract":"Pneumonia is a serious disease that poses a threat to people’s health of all ages. It could happen when people are infected by viruses, fungi, bacteria etc. Typically, Chest X-rays are the first and foremost imaging approach to implement pneumonia detection. This paper introduces the latest research achievements to help those who are new in this field to have basic intuition about AI in pneumonia detection., including Vision Transformers on chest X-ray, a novel model based on RetinaNet, CP_DeepNet, and a novel Efficient NetV2L model. The last part gives some suggestions about the future study of pneumonia detection using Deep learning.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"30 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of technology, in order to improve the user’s driving experience and driving safety, there are more and more vehicle tasks with high delay requirements. Therefore, lots of researchers have paid attention to task offloading scheduling.However, as vehicle tasks become increasingly complex, a single task may consist of multiple subtasks with dependencies between them.The complex data dependencies within them make it more and more difficult to design appropriate task offloading strategies. Considering that this problem is closely related to the scenarios and requirements in the real world, this study focuses on the design of task offloading decisions in the scenario of UAV-assisted vehicle network, in which MEC servers are installed in the macro base station and UAV to provide computing resources for vehicles. We designed a task offloading strategy based on MATD3 algorithm to deal with this problem. Following simulation trials, it is evident that our approach offers notable benefits in terms of both delay and energy usage.
{"title":"Research on UAV-assisted Vehicle networking task unloading strategy based on multi-agent reinforcement learning","authors":"Fanjin Zeng","doi":"10.61173/ceadt415","DOIUrl":"https://doi.org/10.61173/ceadt415","url":null,"abstract":"With the development of technology, in order to improve the user’s driving experience and driving safety, there are more and more vehicle tasks with high delay requirements. Therefore, lots of researchers have paid attention to task offloading scheduling.However, as vehicle tasks become increasingly complex, a single task may consist of multiple subtasks with dependencies between them.The complex data dependencies within them make it more and more difficult to design appropriate task offloading strategies. Considering that this problem is closely related to the scenarios and requirements in the real world, this study focuses on the design of task offloading decisions in the scenario of UAV-assisted vehicle network, in which MEC servers are installed in the macro base station and UAV to provide computing resources for vehicles. We designed a task offloading strategy based on MATD3 algorithm to deal with this problem. Following simulation trials, it is evident that our approach offers notable benefits in terms of both delay and energy usage.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"30 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article analyzes the impact of housing satisfaction from multiple perspectives. And there are no missing values in the data. Factor analysis is used to reduce the dimensionality of variables, integrating multiple factors into five factors for easy analysis. The meanings of the factors are clear, namely: living conditions, family situation, regional economy, experience situation, and social employment quality. The factor is processed using binomial logistic regression, and the prediction effect is relatively satisfactory. Analysis of the parameters shows that the better the current living conditions, the higher the regional economy, the higher the quality of social employment, and the higher the probability of housing satisfaction. By comparing the full variable binomial logistic regression, it was found that the older the model parameters, the better their age and employment status, the larger their per capita living area, and the lower their education level. Unmarried individuals are more likely to be satisfied with their houses, which is consistent with basic knowledge.
{"title":"Research on the Influencing Factors of Housing Satisfaction","authors":"Maimu Yang","doi":"10.61173/p3g1ht41","DOIUrl":"https://doi.org/10.61173/p3g1ht41","url":null,"abstract":"This article analyzes the impact of housing satisfaction from multiple perspectives. And there are no missing values in the data. Factor analysis is used to reduce the dimensionality of variables, integrating multiple factors into five factors for easy analysis. The meanings of the factors are clear, namely: living conditions, family situation, regional economy, experience situation, and social employment quality. The factor is processed using binomial logistic regression, and the prediction effect is relatively satisfactory. Analysis of the parameters shows that the better the current living conditions, the higher the regional economy, the higher the quality of social employment, and the higher the probability of housing satisfaction. By comparing the full variable binomial logistic regression, it was found that the older the model parameters, the better their age and employment status, the larger their per capita living area, and the lower their education level. Unmarried individuals are more likely to be satisfied with their houses, which is consistent with basic knowledge.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"23 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple regression analysis is a statistical method used to examine the relationship between a dependent variable and multiple independent variables. It extends the principles of simple linear regression to accommodate the complexity of real-world data, allowing researchers to study the combined effect of multiple predictors on an outcome of interest. This article provides a comprehensive overview of multiple regression analysis, including its theoretical foundations, practical applications, and key considerations. First, we discuss the basic concept of multiple regression and its historical development, tracing its evolution from simple linear regression. The article then delves into the methodology of multiple regression, covering topics such as model specification, estimation techniques, and model evaluation. Additionally, it explores advanced topics in multiple regression analysis, including multicollinearity, heteroskedasticity, and model selection. Real-world examples and case studies from a variety of fields illustrate the versatility and applicability of multiple regression analysis in empirical research. By providing a thorough understanding of multiple regression, this article aims to provide researchers with the knowledge and tools needed to effectively utilize this statistical technique in their own research.
{"title":"Exploring Multiple Regression Models: Key Concepts and Applications","authors":"Yanbo Ruan","doi":"10.61173/yjpt3s59","DOIUrl":"https://doi.org/10.61173/yjpt3s59","url":null,"abstract":"Multiple regression analysis is a statistical method used to examine the relationship between a dependent variable and multiple independent variables. It extends the principles of simple linear regression to accommodate the complexity of real-world data, allowing researchers to study the combined effect of multiple predictors on an outcome of interest. This article provides a comprehensive overview of multiple regression analysis, including its theoretical foundations, practical applications, and key considerations. First, we discuss the basic concept of multiple regression and its historical development, tracing its evolution from simple linear regression. The article then delves into the methodology of multiple regression, covering topics such as model specification, estimation techniques, and model evaluation. Additionally, it explores advanced topics in multiple regression analysis, including multicollinearity, heteroskedasticity, and model selection. Real-world examples and case studies from a variety of fields illustrate the versatility and applicability of multiple regression analysis in empirical research. By providing a thorough understanding of multiple regression, this article aims to provide researchers with the knowledge and tools needed to effectively utilize this statistical technique in their own research.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advancement of urbanization has brought urban land use change into the spotlight of research. Remote sensing technology, as the primary means of capturing this change, provides valuable data for research. The main research object of this paper is the four classification methods of land use in cities based on remote sensing technology, and it outlines the main roles and applications of these four classification methods. This paper concludes that the visual interpretation method is less efficient but widely used. In addition, the accuracy of supervised classification and deep learning methods for classification is low. Unsupervised classification is simple, but the results differ greatly from reality. The above commonly used land classification methods have their advantages and disadvantages, and their combined use ensures that remote sensing technology provides real-time, effective, and comprehensive land use information for urban planning and management. Finally, this paper looks forward to the combination of remote sensing technology and modern technology, such as artificial intelligence. The integrated technology can provide more accurate and efficient technical support for future urban development.
{"title":"Research on Urban Land Use based on Remote Sensing Technology","authors":"Xiaochun Xu","doi":"10.61173/9smr3z05","DOIUrl":"https://doi.org/10.61173/9smr3z05","url":null,"abstract":"The advancement of urbanization has brought urban land use change into the spotlight of research. Remote sensing technology, as the primary means of capturing this change, provides valuable data for research. The main research object of this paper is the four classification methods of land use in cities based on remote sensing technology, and it outlines the main roles and applications of these four classification methods. This paper concludes that the visual interpretation method is less efficient but widely used. In addition, the accuracy of supervised classification and deep learning methods for classification is low. Unsupervised classification is simple, but the results differ greatly from reality. The above commonly used land classification methods have their advantages and disadvantages, and their combined use ensures that remote sensing technology provides real-time, effective, and comprehensive land use information for urban planning and management. Finally, this paper looks forward to the combination of remote sensing technology and modern technology, such as artificial intelligence. The integrated technology can provide more accurate and efficient technical support for future urban development.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the progress of science and technology, the material life of human society is becoming more and more perfect. Still, it also causes the human living environment to become more and more harsh. Water resources are polluted to different degrees, affecting the sustainable development of ecology. Water resource management based on emerging technologies is urgent. The purpose of this paper is to analyze and evaluate the pollutants and types of pollution in the water body of Poyang Lake based on multi-source remote sensing data through the pollution indicators of colored soluble organic matter pollution, water body eutrophication, and heavy metal salt pollution. This paper concludes that Poyang Lake faces problems such as high organic pollution during the abundant water period, medium eutrophication of the water body, and excessive cadmium and manganese heavy metal salts. This paper finds that establishing relatively different pollution indicator systems is more conducive to analyzing and evaluating water resource pollution in different geographical areas. With the progress of remote sensing science, more and more remote sensing technology is applied to the governance and management process of water resource pollution.
{"title":"Integrating Multi-source Remote Sensing Technology to Improve Water Resource Management Methods at Poyang Lake","authors":"Xinye Chen, Pengcheng Feng, Weijun Feng, Zhuozhuo Shao","doi":"10.61173/ck4av294","DOIUrl":"https://doi.org/10.61173/ck4av294","url":null,"abstract":"With the progress of science and technology, the material life of human society is becoming more and more perfect. Still, it also causes the human living environment to become more and more harsh. Water resources are polluted to different degrees, affecting the sustainable development of ecology. Water resource management based on emerging technologies is urgent. The purpose of this paper is to analyze and evaluate the pollutants and types of pollution in the water body of Poyang Lake based on multi-source remote sensing data through the pollution indicators of colored soluble organic matter pollution, water body eutrophication, and heavy metal salt pollution. This paper concludes that Poyang Lake faces problems such as high organic pollution during the abundant water period, medium eutrophication of the water body, and excessive cadmium and manganese heavy metal salts. This paper finds that establishing relatively different pollution indicator systems is more conducive to analyzing and evaluating water resource pollution in different geographical areas. With the progress of remote sensing science, more and more remote sensing technology is applied to the governance and management process of water resource pollution.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"103 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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