Machine Learning: New Ideas and Tools in Environmental Science and Engineering 7032 01:09:00 如何识别陆地与海洋气溶胶来源? 3784 53:00 Carbon source/sink functions of rice paddies: Biogeochemical processes underlying the trade-off 1937 01:28:00 ...
Such techniques are collectively known as machine learning. In this report, a survey of recent applications of machine learning in environmental engineering is presented. In particular, the focus is on the topics of water resources management, flood prediction, rainfall-runoff modeling, wastewater ...
Firstly, the paper reviews the development of machine learning and investigates the current research status of machine learning in the field of environmental engineering. Secondly, the paper elaborates on the applications and practical cases of machine learning in environmental monitoring, natural disaster...
在Chemical Reviews,Environmental Science & Technology,Water Research,Applied Catalysis B.等期刊上发表论文多篇,并作为项目负责人获得美国国家科学基金会等六项极具竞争力的研究资助,及多项美国联邦政府、州政府以及行业研究项目。担任ACS ES&T Water期刊的主题编辑和Frontiers of Environmental Science & Engineering(...
Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data." Our news editors obtained a quote from the research from the School of Civil and Environmental Engineering, "However, relationships between the features of wastewater datasets are ...
Renovation in environmental, social and governance (ESG) research: the application of machine learning Purpose - Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investin... AY Zhang,JH Zhang,H Zhou 被引量: ...
Artificial Intelligence (AI) can ameliorate but the literature suggests that the deployment of Machine Learning (ML) techniques in soil research is concentrated mostly in developed countries. The potential of ML in managing soil pollution from complex mixture of heavy metals, petroleum hydrocarbons, ...
Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. We demonstrate how machine-learning methods can inform the efficient use of these limited resources while accounting for real-world concerns, s
【关键句】AI deep learning is well-suited to find patterns in thecomplexity of potentially thousands of confounders.(第五段最后一句译文:人工智能深度学习非常适合在潜在的成千上万的混杂干扰因素中探索模式。【解题思路】根据第五段倒数第二句“When working withreal-world data, confounders could number in...
closely related task. In the context of bioprocess modeling, transfer learning could be explored to enhance the predictive capabilities of the models by pretraining them on a large dataset, such as general time series data of bioreactors, before fine-tuning them on the specific task of ...