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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Emergence of microplastics in the aquatic ecosystem and their potential effects on health risks: The insights into Vietnam

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Journal of environmental Management 2023年 344卷 118499页

作者： Nguyen, Minh-Ky Lin, Chitsan Nguyen, Hoang-Lam Le, Van-Re KL, Priya Singh, Jagpreet Chang, Soon W. Um, Myoung-Jin Nguyen, D. Duc Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Faculty of Environment and Natural Resources Nong Lam University Ho Chi Minh City700000 Viet Nam Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Civil Engineering McGill University Montreal Canada Ho Chi Minh City700000 Viet Nam Department of Civil Engineering TKM College of Engineering Kollam691005 India Department of Chemistry University Centre for Research & Development Chandigarh University Punjab Mohali140413 India Department of Civil & Energy System Engineering Kyonggi University Suwon Korea Republic of Faculty of Environmental and Food Engineering Nguyen Tat Thanh University 300A Nguyen Tat Thanh District 4 Ho Chi Minh City755414 Viet Nam

The increase of microplastic contamination in Vietnam is a growing concern due to various domestic, agricultural, and industrial activities. The use of plastic mulch and sludge application in agricultural farmland, textile production, daily consumer items, cleaning agents, and health/personal care products contribute significantly to the increasing microplastic pollution in the aquatic ecosystem. The concentration of microplastics reported in surface water ranged from 0.35 to 519,000 items m−3, with fibers and fragments being the most prevalent shapes. Notably, the high concentration of microplastics was observed in lakes, canals, and megacities such as Ha Noi and Ho Chi Minh City, which poses potential health risks to the local community via drinking-water supply and food chains. As an emerging pollutant, MPs are the transport vectors for contaminants in environmental matrices that act as a carrier of hazardous pollutants, release toxic compounds, and evenly aggregate/accumulate in biota. Recent studies have reported the presence of microplastics in various marine organisms, including fish and shellfish, highlighting the risk of ingestion of these particles by humans and wildlife. Thus, it is imperative to monitor microplastic contamination in the ecosystem to provide helpful information for the government and local communities. Efforts should be taken to reduce microplastic pollution at the source to minimize potential effects on ecological and health safety. This review paper emphasizes the urgent need for further research on microplastic pollution in Vietnam and highlights potential solutions to mitigate this emerging environmental threat. Keyworks: single-use plastics;microplastics;ecosystems;plastic waste;health risk;ecological and health safety;pollution mitigation. © 2023 Elsevier Ltd

关键词： Microplastic

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Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

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Big Earth Data 2024年第2期8卷 189-230页

作者： Alexander Mahura Alexander Baklanov Risto Makkonen Michael Boy Tuukka Petäjä Hanna KLappalainen Roman Nuterman Veli-Matti Kerminen Stephen R.Arnold Markus Jochum Anatoly Shvidenko Igor Esau Mikhail Sofiev Andreas Stohl Tuula Aalto Jianhui Bai Chuchu Chen Yafang Cheng Oxana Drofa Mei Huang Leena Järvi Harri Kokkola Rostislav Kouznetsov Tingting Li Piero Malguzzi Sarah Monks Mads Bruun Poulsen Steffen M.Noe Yuliia Palamarchuk Benjamin Foreback Petri Clusiu Till Andreas Soya Rasmussen Jun She Jens Havskov Sørensen Dominick Spracklen Hang Su Juha Tonttila Siwen Wang Jiandong Wang Tobias Wolf-Grosse Yongqiang Yu Qing Zhang Wei Zhang Wen Zhang Xunhua Zheng Siqi Li Yong Li Putian Zhou Markku Kulmala Faculty of Science Institute for Atmospheric and Earth System Research(INAR)/PhysicsUniversity of Helsinki(UHEL)HelsinkiFinland Science and Innovation Department World Meteorological Organization(WMO)GenevaSwitzerland Niels Bohr Institute University of CopenhagenCopenhagenDenmark Climate System Research Finnish Meteorological Institute(CSR-FMI)HelsinkiFinland School of Engineering Sciences Lappeenranta University of TechnologyLappeenrantaFinland Institute for Climate and Atmospheric Science School of Earth and EnvironmentUniversity of LeedsLeedsUK Biodiversity and Natural Resources Program International Institute for Applied Systems Analysis(IIASA)LaxenburgAustria Department of Physics and Technology UiT-The Arctic University of NorwayTromsoNorway Nansen Environmental and Remote Sensing Center(NERSC) BergenNorway Atmospheric Composition Finnish Meteorological Institute(AC-FMI)HelsinkiFinland Norwegian Institute for Air Research(NILU) KjellerNorway LAGEO Institute of Atmospheric PhysicsChinese Academy of Sciences(IAP-CAS)BeijingChina Max Planck Institute for Chemistry(MPI-Chem) MainzGermany Institute of Atmospheric Sciences and Climate National Research Council of Italy(CNR-15SAC)BolognaItaly Institute of Geographic Sciences and Natural Resources Research(IGSNRR) Chinese Academy of Sciences(CAS)BeijingChina Faculty of Science Helsinki Institute of Sustainability Science(HELSUS)University of Helsinki(UHEL)HelsinkiFinland Atmospheric Research Centre of Eastern Finland Finnish Meteorological Institute(ARCEF-FMI)KuopioFinland Institute of Atmospheric Physics Chinese Academy of Sciences(IAP-CAS)BeijingChina Department of Plant Physiology Estonian University of Life Sciences(EULS)Institute of Agricultural and Environmental Sciences(IAES)TartuEstonia Danish Meteorological Institute(DMI) CopenhagenDenmark State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric Physics(IAP)Chinese Academy of Sciences(CAS)BeijingChina Uni

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research *** PEEX MP has more than 30 models and is directed towards seamless envir-onmental *** main focus area is the Arctic-boreal regions and *** models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and *** paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical *** employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are *** virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are *** over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,*** population,envir-onment and climate in the PEEX *** work was also one of the last projects of *** Zilitinkevich,who passed away on 15 February *** the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

关键词： Multi-scale and-processes modelling concept seamless coupling high-performance computing data infrastructure virtual research platforms

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Developing a Three-Dimensional Urban Surface Model for Spatiotemporal Analysis of Thermal Comfort with Respect to Street Direction

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SSRN 2022年

作者： Yun, Seok Hwan Lee, Dong Kun Piao, Zheng Gang Park, Chae Yeon Kim, Sang Hyuck Kim, Eun Sub Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Social Systems Division National Institute for Environmental Studies 16-2 Onogawa Ibaraki Tsukuba305-8506 Japan

Understanding the urban heat problem, which is intensifying owing to urbanization and climate change, has become a great challenge. Evaluating the thermal environment of urban spaces and predicting changes based on urban planning are essential for developing optimal urban cooling plans. Models using numerical methods are efficient and flexible for accurately simulating thermal comfort in various spatial arrangements in urban areas. In this study, we developed a three-dimensional urban surface model to evaluate the thermal comfort of humans by reflecting complex urban environments. We analyzed the thermal comfort according to the angular change of east–west streets, which are vulnerable to heat, and found that a change of 12.5° has a significant effect on the heat felt by the person on the sidewalk. The results suggest that spatiotemporal thermal comfort simulations considering the orientation of the streets and analytical planning are necessary to efficiently cool the city. This model can be used to develop efficient cooling strategies to achieve sustainable cities. © 2022, The Authors. All rights reserved.

关键词： Thermal comfort

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Elimination of antibiotic resistance genes and control of horizontal transfer risk by UV-based treatment of drinking water: A mini review

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Frontiers of environmental Science & engineering 2019年第3期13卷 3-11页

作者： Virender K.Sharma Xin Yu Thomas J.McDonald Chetan Jinadatha Dionysios D.Dionysiou Mingbao Feng Department of Environmental and Occupational Health School of Public Health Texas A&M University College Station TX 77843 USA Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China Central Texas Veterans Health Care System Temple TX 76504 USA College of Medicine Texas A&M Health Science Center Bryan TX 77807 USA Environmental Engineering and Science Program Department of Chemical and Environmental Engineering (DChEE) 705 Engineering Research Center University of Cincinnati Cincinnati OH 45221 USA

Antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) have been recognized as one of the biggest public health issues of the 21 st century. Both ARB and ARGs have been determined in water after treatment with conventional disinfectants. Ultraviolet (UV) technology has been seen growth in application to disinfect the water. However, UV method alone is not adequate to degrade ARGs in water. Researchers are investigating the combination of UV with other oxidants (chlorine, hydrogen peroxide (H2O2), peroxymonosulfate (PMS), and photocatalysts) to harness the high reactivity of produced reactive species (C1-, C1O -, Cl2-,-OH, and SO4-_) in such processes with constituents of cell (e.g., deoxyribonucleic acid (DNA) and its components) in order to increase the degradation efficiency of ARGs. This paper briefly reviews the current status of different UV-based treatments (UV/chlorination, UV/H2O2, UV/PMS, and UV-photocatalysis) to degrade ARGs and to control horizontal gene transfer (HGT) in water. The review also provides discussion on the mechanism of degradation of ARGs and application of q-PCR and gel electrophoresis to obtain insights of the fate of ARGs during UV-based treatment processes.

关键词： Antibiotic resistance bacteria Advanced oxidation processes Disinfection Reactive chlorine species Sulfate radicals Reactive oxygen species

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Evaluation of antibiotic-resistant bacteria in home kitchens and bathrooms: Is there a link between home disinfectant use and antibiotic resistance?

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American Journal of Infection Control 2023年第11期51卷 A158-A163页

作者： Rutala, William A. Weber, David J. Barbee, Susan L. Gergen, Maria F. Sobsey, Mark D. Samsa, Gregory P. Sickbert-Bennett, Emily E. Division of Infectious Diseases University of North Carolina School of Medicine Chapel Hill NC United States NC Statewide Program for Infection Control and Epidemiology University of North Carolina School of Medicine Chapel Hill NC United States Department of Infection Prevention University of North Carolina Health Care System Chapel Hill NC United States Department of Engineering and Environmental Sciences University of North Carolina School of Public Health Chapel Hill NC United States

Objective: To determine the relationship between home disinfectant use and the prevalence of antibiotic resistance among environmental isolates of human pathogens. Methods: Bacteria were cultured from 5 kitchen and 5 bathroom sites using quantitative methods. Antibiotic susceptibility was determined by standard methods. Home disinfectant use was assessed via a questionnaire. Results: The overall total mean log₁₀ counts (total CFU) for the kitchen and bathroom were 4.31 and 4.88, respectively. Gram-positive bacteria were more common in the bathroom (4.05) than in the kitchen (3.60), while Gram-negative bacilli were more common in the kitchen (4.23) than in the bathroom (3.86). The sink and bath drains were the most contaminated sites with 6.16-log₁₀ of total CFU and 6.6-log₁₀ in the kitchen and bathroom, respectively. Households reported cleaning frequency with a variety of commercial products. Most respondents used antibacterial products (eg, soaps, surface disinfectants) in the home. Antibiotic-resistant pathogens were infrequently isolated in the homes evaluated. Conclusions: Compared to pathogens causing community-acquired clinical infections in the ICARE study, pathogens isolated from households are less likely to demonstrate antibiotic resistance. In addition, no relationship between antibacterial use or frequency of cleaning or disinfection and antibiotic resistance was revealed. © 2023 Association for Professionals in Infection Control and Epidemiology, Inc.

关键词： Antimicrobial Germicides Microorganisms

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Landscape Design for Improved Thermal Environment: An Optimized Tree Arrangement Design for Climate-Responsive Outdoor Spaces in Residential Buildings

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SSRN 2023年

作者： Kim, Ji Yeon Park, Chae Yeon Hyun, Jung Hee Kim, Sang Hyuck Yun, Seok Hwan Lee, Dong Kun Interdisciplinary Program in Landscape Architecture Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul Korea Republic of Specialized Graduate School of Intelligent Eco-Science Seoul National University Seoul Korea Republic of Smart Green Solution team SK forest Seoul Korea Republic of National Institute for Environmental Studies 16-2 Onogawa Ibaraki Tsukuba305-8506 Japan Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul Korea Republic of

The heat reduction effect of trees has been investigated through numerical simulations;however, there are still challenges to applying the scientific results to the planning process due to the model's complexity and the computational resources required. This study investigates a rapid spatial evaluation method for heat stress potential, measured by mean radiant temperature (MRT), by decomposing radiation into sub-radiation using a multilayer MRT model. This method also enables the reproduction of optimized layouts considering the effect of tree arrangement in residential buildings. Multi-objectives were achieved through an evolutionary algorithm, resulting in more effective design layouts combining tree types and arrangements, all within a standard budget. By adopting this study's approach, landscape designers can create climate-responsive tree layouts with reduced heat exposure and generate customized planting designs tailored to their preferences. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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