Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales
出版年份 2019 全文链接
标题
Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales
作者
关键词
-
出版物
Water
Volume 11, Issue 2, Pages 339
出版商
MDPI AG
发表日期
2019-02-18
DOI
10.3390/w11020339
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management
- (2018) Nadia Schou Vorndran Lund et al. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY
- The State of U.S. Urban Water: Data and the Energy-Water Nexus
- (2018) Christopher M. Chini et al. WATER RESOURCES RESEARCH
- Urban Surface Water Quality, Flood Water Quality and Human Health Impacts in Chinese Cities. What Do We Know?
- (2018) Yuhan Rui et al. Water
- Watershed Models for Development and Implementation of Total Maximum Daily Loads
- (2018) Deva K. Borah et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers
- (2018) Angelika M. Meyer et al. SCIENCE OF THE TOTAL ENVIRONMENT
- The Potential of Knowing More: A Review of Data-Driven Urban Water Management
- (2017) Sven Eggimann et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Determination of water environment standards based on water quality criteria in China: Limitations and feasibilities
- (2017) Tieyu Wang et al. JOURNAL OF ENVIRONMENTAL SCIENCES
- Modelling transitions in urban water systems
- (2017) W. Rauch et al. WATER RESEARCH
- Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector
- (2017) Juan Ponce Romero et al. Sustainability
- Water quality monitoring strategies — A review and future perspectives
- (2016) S. Behmel et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Treatment technologies and mechanisms for three odorants at trace level: IPMP, IBMP, and TCA
- (2015) Xin Li et al. ENVIRONMENTAL TECHNOLOGY
- Time-series clustering – A decade review
- (2015) Saeed Aghabozorgi et al. INFORMATION SYSTEMS
- Future water quality monitoring — Adapting tools to deal with mixtures of pollutants in water resource management
- (2015) Rolf Altenburger et al. SCIENCE OF THE TOTAL ENVIRONMENT
- NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set
- (2015) Malika Charrad et al. Journal of Statistical Software
- Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
- (2014) Fionn Murtagh et al. JOURNAL OF CLASSIFICATION
- Challenges of Big Data analysis
- (2014) Jianqing Fan et al. National Science Review
- Application of interval clustering approach to water quality evaluation
- (2013) H. Wong et al. JOURNAL OF HYDROLOGY
- Three-dimensional hydrodynamic and water quality model for TMDL development of Lake Fuxian, China
- (2012) Lei Zhao et al. JOURNAL OF ENVIRONMENTAL SCIENCES
- What is the expectation maximization algorithm?
- (2008) Chuong B Do et al. NATURE BIOTECHNOLOGY
- How polluted is the Yangtze river? Water quality downstream from the Three Gorges Dam
- (2008) Beat Müller et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Hierarchical Clustering of Massive, High Dimensional Data Sets by Exploiting Ultrametric Embedding
- (2008) Fionn Murtagh et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
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