A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations
出版年份 2021 全文链接
标题
A stack-based set inversion model for smart water, carbon and ecological assessment in urban agglomerations
作者
关键词
Water, Carbon and ecological footprints, Smart evaluation and prediction, Ensemble inversion model, Urban agglomeration, Yangtze river
出版物
Journal of Cleaner Production
Volume 319, Issue -, Pages 128665
出版商
Elsevier BV
发表日期
2021-08-14
DOI
10.1016/j.jclepro.2021.128665
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。- Analysis of water–carbon–ecological footprints and resource–environment pressure in the Triangle of Central China
- (2021) Yizhong Chen et al. ECOLOGICAL INDICATORS
- Water Security Assessment for the Contiguous United States Using Water Footprint Concepts
- (2020) Anoop Valiya Veettil et al. GEOPHYSICAL RESEARCH LETTERS
- Mitigating the carbon footprint and improving productivity of ruminant livestock agriculture using a red seaweed
- (2020) Robert D. Kinley et al. JOURNAL OF CLEANER PRODUCTION
- Machine learning models for ecological footprint prediction based on energy parameters
- (2020) Radmila Janković et al. NEURAL COMPUTING & APPLICATIONS
- What is a footprint? A conceptual analysis of environmental footprint indicators
- (2020) Jan Matuštík et al. JOURNAL OF CLEANER PRODUCTION
- Urban expansion and drying climate in an urban agglomeration of east China
- (2019) Ming Luo et al. GEOPHYSICAL RESEARCH LETTERS
- The evolution of the ecological footprint and its relationship with the urban development of megacities in Western China: The case of Xi'an
- (2019) Yi Yang et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Environmental footprint family to address local to planetary sustainability and deliver on the SDGs
- (2019) Davy Vanham et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Dynamic assessment and forecast of urban water ecological footprint based on exponential smoothing analysis
- (2018) Yin Su et al. JOURNAL OF CLEANER PRODUCTION
- IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems
- (2018) Chenjie Cao et al. KNOWLEDGE-BASED SYSTEMS
- Pharmaceutical grey water footprint: Accounting, influence of wastewater treatment plants and implications of the reuse
- (2018) Isabel Martínez-Alcalá et al. WATER RESEARCH
- Learning rules for multi-label classification: a stacking and a separate-and-conquer approach
- (2016) Eneldo Loza Mencía et al. MACHINE LEARNING
- A linguistic entropy weight method and its application in linguistic multi-attribute group decision making
- (2016) Yonghuan He et al. NONLINEAR DYNAMICS
- Multitask learning for host–pathogen protein interactions
- (2013) Meghana Kshirsagar et al. BIOINFORMATICS
- An urban metabolism and ecological footprint assessment of Metro Vancouver
- (2013) Jennie Moore et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model
- (2012) Hsiao-Tien Pao et al. ENERGY
- Carbon, Land, and Water Footprint Accounts for the European Union: Consumption, Production, and Displacements through International Trade
- (2012) Kjartan Steen-Olsen et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- A Footprint Family extended MRIO model to support Europe's transition to a One Planet Economy
- (2012) Alessandro Galli et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Integrating Ecological, Carbon and Water footprint into a “Footprint Family” of indicators: Definition and role in tracking human pressure on the planet
- (2011) Alessandro Galli et al. ECOLOGICAL INDICATORS
- Urban total ecological footprint forecasting by using radial basis function neural network: A case study of Wuhan city, China
- (2009) X.M. Li et al. ECOLOGICAL INDICATORS
- Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
- (2009) J.D. Rodriguez et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A notion of task relatedness yielding provable multiple-task learning guarantees
- (2008) Shai Ben-David et al. MACHINE LEARNING
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