Big Data in Earth system science and progress towards a digital twin
暂无分享,去创建一个
Y. Ran | Feng Liu | Xin Li | Jianbing Su | Chunlin Huang | S. Yuan | Min Feng | Huadong Guo | Qing Xiao | Yang Su | Huanfeng Shen | Huanfeng Shen
[1] Huanfeng Shen,et al. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems , 2023, Science China Earth Sciences.
[2] G. Dax,et al. Compression Supports Spatial Deep Learning , 2023, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[3] T. Lenton,et al. Exceeding 1.5°C global warming could trigger multiple climate tipping points , 2022, Science.
[4] M. Latif. The roadmap of climate models , 2022, Nature Computational Science.
[5] G. Beroza,et al. Deep-learning seismology , 2022, Science.
[6] Fa-Ju Chen,et al. Measuring and evaluating SDG indicators with Big Earth Data. , 2022, Science bulletin.
[7] Wenchong Tian,et al. Combined Sewer Overflow and Flooding Mitigation Through a Reliable Real‐Time Control Based on Multi‐Reinforcement Learning and Model Predictive Control , 2022, Water Resources Research.
[8] Brodie C. Pearson,et al. The small scales of the ocean may hold the key to surprises , 2022, Nature Climate Change.
[9] H. Tomita,et al. Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation , 2022, Journal of Advances in Modeling Earth Systems.
[10] Yufang Jin,et al. Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting , 2022, Journal of Computational Physics.
[11] N. Hovakimyan,et al. Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[12] C. Pain,et al. Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models , 2022, Journal of Scientific Computing.
[13] Weihua An,et al. Causal Network Analysis , 2022, The Annual review of sociology.
[14] G. Carmichael,et al. The future of Earth system prediction: Advances in model-data fusion , 2022, Science advances.
[15] P. Gaspar,et al. Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal , 2022, Agricultural Water Management.
[16] S. Sepasgozar,et al. City Digital Twin Concepts: A Vision for Community Participation , 2022, The 3rd Built Environment Research Forum.
[17] S. Athey,et al. Stable learning establishes some common ground between causal inference and machine learning , 2022, Nature Machine Intelligence.
[18] N. Oza,et al. NASA Earth Science Technology for Earth System Digital Twins (ESDT) , 2022 .
[19] S. Ong,et al. State-of-the-art survey on digital twin implementations , 2022, Advances in Manufacturing.
[20] M. Cannon,et al. Implementing an Open & FAIR data sharing policy—A case study in the earth and environmental sciences , 2022, Learn. Publ..
[21] Julio Amador Díaz López,et al. Data Learning: Integrating Data Assimilation and Machine Learning , 2021, J. Comput. Sci..
[22] J. Brajard,et al. Super-resolution data assimilation , 2021, Ocean Dynamics.
[23] Massimo Bonavita,et al. Machine Learning for Earth System Observation and Prediction , 2021, Bulletin of the American Meteorological Society.
[24] L. Bruzzone,et al. Self-Supervised Change Detection in Multiview Remote Sensing Images , 2021, IEEE Transactions on Geoscience and Remote Sensing.
[25] Chaehyeon Lee,et al. Contrastive Self-Supervised Learning With Smoothed Representation for Remote Sensing , 2022, IEEE Geoscience and Remote Sensing Letters.
[26] A. Wexler,et al. Conservation laws in a neural network architecture: Enforcing the atom balance of a Julia-based photochemical model (v0.2.0) , 2021, Geoscientific Model Development.
[27] E. Parson. Geoengineering: Symmetric precaution. , 2021, Science.
[28] D. Zheng,et al. Information geography: The information revolution reshapes geography , 2021, Science China Earth Sciences.
[29] J. Ruiz,et al. Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction , 2021, Nonlinear Processes in Geophysics.
[30] Alexander Lavin,et al. Digital Twin Earth -- Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators , 2021, 2110.07100.
[31] Y. C. E. Yang,et al. Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach , 2021, Water Resources Research.
[32] W. Xiao,et al. Origin, Accretion, and Reworking of Continents , 2021, Reviews of Geophysics.
[33] Honglin He,et al. Boosting geoscience data sharing in China , 2021, Nature Geoscience.
[34] Pavel Ugwitz,et al. A Comparison of Monoscopic and Stereoscopic 3D Visualizations: Effect on Spatial Planning in Digital Twins , 2021, Remote. Sens..
[35] Oriol Vinyals,et al. Highly accurate protein structure prediction with AlphaFold , 2021, Nature.
[36] Alberto Arribas,et al. Quantifying causal pathways of teleconnections , 2021, Bulletin of the American Meteorological Society.
[37] D. Prelec,et al. Human social sensing is an untapped resource for computational social science , 2021, Nature.
[38] Jianwei Ma,et al. Deep Learning for Geophysics: Current and Future Trends , 2021, Reviews of Geophysics.
[39] Huadong Guo,et al. Big Earth Data: a practice of sustainability science to achieve the Sustainable Development Goals. , 2021, Science bulletin.
[40] Yuanlai Cui,et al. A reinforcement learning approach to irrigation decision-making for rice using weather forecasts , 2021 .
[41] Klaus Diepold,et al. Multi-agent deep reinforcement learning: a survey , 2021, Artificial Intelligence Review.
[42] Raia Hadsell,et al. Skilful precipitation nowcasting using deep generative models of radar , 2021, Nature.
[43] P. Cox,et al. Overshooting tipping point thresholds in a changing climate , 2021, Nature.
[44] A. Geer,et al. Learning earth system models from observations: machine learning or data assimilation? , 2021, Philosophical Transactions of the Royal Society A.
[45] Prabhat,et al. Physics-informed machine learning: case studies for weather and climate modelling , 2021, Philosophical Transactions of the Royal Society A.
[46] Q. Cheng,et al. The Deep-Time Digital Earth program: data-driven discovery in geosciences , 2021, National science review.
[47] Adrienne Raglin,et al. Causal inference for time series analysis: problems, methods and evaluation , 2021, Knowledge and Information Systems.
[48] Torsten Hoefler,et al. The digital revolution of Earth-system science , 2021, Nature Computational Science.
[49] Peter Bauer,et al. A digital twin of Earth for the green transition , 2021, Nature Climate Change.
[50] A. Petrov,et al. Rethinking Arctic sustainable development agenda through indigenizing UN sustainable development goals , 2021, International Journal of Sustainable Development & World Ecology.
[51] F. Radicchi,et al. Detecting Climate Teleconnections With Granger Causality , 2020, Geophysical Research Letters.
[52] Naoto Yokoya,et al. More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.
[53] J. Leinonen,et al. Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.
[54] X. Jia,et al. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles , 2020, Trans. Data Sci..
[55] Pierre Baldi,et al. Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems. , 2019, Physical review letters.
[56] A. Wills,et al. Physics-informed machine learning , 2021, Nature Reviews Physics.
[57] M. Jain,et al. Unravelling the teleconnections between ENSO and dry/wet conditions over India using nonlinear Granger causality , 2021 .
[58] Xinrun Wang,et al. Efficient Reservoir Management through Deep Reinforcement Learning , 2020, ArXiv.
[59] Javier G. P. Gamarra,et al. The importance of sharing global forest data in a world of crises , 2020, Scientific Data.
[60] Ji Zhao,et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF , 2020 .
[61] S. E. Haupt,et al. Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences , 2020, Bulletin of the American Meteorological Society.
[62] Alexander Y. Sun,et al. Optimal carbon storage reservoir management through deep reinforcement learning , 2020, Applied Energy.
[63] Ian Goodfellow,et al. Generative adversarial networks , 2020, Commun. ACM.
[64] L. Dicks,et al. Biofilm dynamics: linking in situ biofilm biomass and metabolic activity measurements in real-time under continuous flow conditions , 2020, NPJ biofilms and microbiomes.
[65] C. Fletcher,et al. Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada , 2020, Hydrology and Earth System Sciences.
[66] Yu Liu,et al. An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data , 2020, Remote. Sens..
[67] Y. Sawada,et al. Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration , 2020, Hydrology and Earth System Sciences.
[68] P. Voosen. Europe builds 'digital twin' of Earth to hone climate forecasts. , 2020, Science.
[69] Casper Solheim Bojer,et al. Kaggle forecasting competitions: An overlooked learning opportunity , 2020, ArXiv.
[70] N. Zhou. Intelligent Control of Agricultural Irrigation Based on Reinforcement Learning , 2020, Journal of Physics: Conference Series.
[71] Jian Peng,et al. When causal inference meets deep learning , 2020, Nature Machine Intelligence.
[72] M. Sahimi,et al. Machine learning in geo- and environmental sciences: From small to large scale , 2020, Advances in Water Resources.
[73] Juanzhen Sun,et al. Smartphone pressure data: quality control and impact on atmospheric analysis , 2020, Atmospheric Measurement Techniques.
[74] Brian D. Davison,et al. Applications of artificial intelligence for disaster management , 2020, Natural Hazards.
[75] Barbara Unmüßig. Geoengineering , 2020, Forschungsjournal Soziale Bewegungen.
[76] M. Feng,et al. Land cover mapping toward finer scales. , 2020, Science Bulletin.
[77] Xin Li,et al. Harmonizing models and observations: Data assimilation in Earth system science , 2020, Science China Earth Sciences.
[78] Abhiram Mullapudi,et al. Deep reinforcement learning for the real time control of stormwater systems , 2020 .
[79] C. Hsieh,et al. Causal effects of population dynamics and environmental changes on spatial variability of marine fishes , 2020, Nature Communications.
[80] C. Lowry,et al. Improving Hydrological Models With the Assimilation of Crowdsourced Data , 2020, Water Resources Research.
[81] P. Nowack,et al. Causal networks for climate model evaluation and constrained projections , 2020, Nature Communications.
[82] J. Z. Kolter,et al. Overfitting in adversarially robust deep learning , 2020, ICML.
[83] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[84] Jing Luo,et al. Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images , 2020 .
[85] Omer San,et al. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective , 2019, IEEE Access.
[86] I. Otto,et al. Social tipping dynamics for stabilizing Earth’s climate by 2050 , 2020, Proceedings of the National Academy of Sciences.
[87] Douglas H. Erwin,et al. A high-resolution summary of Cambrian to Early Triassic marine invertebrate biodiversity , 2020, Science.
[88] T. Lenton,et al. The emergence and evolution of Earth System Science , 2020, Nature Reviews Earth & Environment.
[89] Ali Ramadhan,et al. Universal Differential Equations for Scientific Machine Learning , 2020, ArXiv.
[90] Demis Hassabis,et al. Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.
[91] Dongxiao Zhang,et al. Deep Learning of Subsurface Flow via Theory-guided Neural Network , 2019, Journal of Hydrology.
[92] Saeid Nahavandi,et al. Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications , 2018, IEEE Transactions on Cybernetics.
[93] Christopher O. Justice,et al. No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework , 2019 .
[94] Ghaleb Abdulla,et al. Deep learning predictions of sand dune migration , 2019, ArXiv.
[95] Markus Reichstein,et al. Physics‐Constrained Machine Learning of Evapotranspiration , 2019, Geophysical Research Letters.
[96] Daniela Fogli,et al. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications , 2019, IEEE Access.
[97] Anuj Karpatne,et al. Process‐Guided Deep Learning Predictions of Lake Water Temperature , 2019, Water Resources Research.
[98] Yuanyuan Zha,et al. A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation , 2019, Advances in Water Resources.
[99] Shian-Jiann Lin,et al. DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains , 2019, Progress in Earth and Planetary Science.
[100] Melba M. Crawford,et al. Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data , 2019, Remote Sensing of Environment.
[101] Wolfram Barfuss,et al. Deep reinforcement learning in World-Earth system models to discover sustainable management strategies , 2019, Chaos.
[102] Bernhard Schölkopf,et al. Inferring causation from time series in Earth system sciences , 2019, Nature Communications.
[103] Lei Ma,et al. Deep learning in remote sensing applications: A meta-analysis and review , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[104] Manzhu Yu,et al. Big Earth data analytics: a survey , 2019, Big Earth Data.
[105] He Zhang,et al. Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.
[106] Maarten V. de Hoop,et al. Machine learning for data-driven discovery in solid Earth geoscience , 2019, Science.
[107] S. Mystakidis,et al. Metaverse , 2019, Interference.
[108] Judea Pearl,et al. The seven tools of causal inference, with reflections on machine learning , 2019, Commun. ACM.
[109] M H Barendrecht,et al. The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model , 2019, Water resources research.
[110] Prabhat,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[111] Ning Zhang,et al. Deep-learning-based seismic data interpolation: A preliminary result , 2019, GEOPHYSICS.
[112] Dino Sejdinovic,et al. Detecting and quantifying causal associations in large nonlinear time series datasets , 2017, Science Advances.
[113] C. Folke,et al. Anthropocene risk , 2019, Nature Sustainability.
[114] Sebastian Scher,et al. Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning , 2018, Geophysical Research Letters.
[115] Peter Gerstoft,et al. Machine Learning in Seismology: Turning Data into Insights , 2018, Seismological Research Letters.
[116] A. Crane-Droesch. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture , 2018, Environmental Research Letters.
[117] Matthew E. Taylor,et al. A survey and critique of multiagent deep reinforcement learning , 2018, Autonomous Agents and Multi-Agent Systems.
[118] O. Boucher,et al. Evaluating climate geoengineering proposals in the context of the Paris Agreement temperature goals , 2018, Nature Communications.
[119] Zefeng Li,et al. Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning , 2018, Geophysical Research Letters.
[120] Lucy Marshall,et al. Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation , 2018 .
[121] Fei Tao,et al. Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.
[122] Gary Marcus,et al. Deep Learning: A Critical Appraisal , 2018, ArXiv.
[123] Chung-Kang Peng,et al. Causal decomposition in the mutual causation system , 2017, Nature Communications.
[124] Chaopeng Shen,et al. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists , 2017, Water Resources Research.
[125] G. Evensen,et al. Data assimilation in the geosciences: An overview of methods, issues, and perspectives , 2017, WIREs Climate Change.
[126] Bernhard Schölkopf,et al. Learning causality and causality-related learning: some recent progress. , 2018, National science review.
[127] Zhi-Hua Zhou,et al. A brief introduction to weakly supervised learning , 2018 .
[128] Huadong Guo,et al. Big Earth data: A new frontier in Earth and information sciences , 2017 .
[129] Joan Bruna,et al. Mathematics of Deep Learning , 2017, ArXiv.
[130] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[131] Anuj Karpatne,et al. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling , 2017, ArXiv.
[132] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[133] G. Salvucci,et al. Confounding factors in determining causal soil moisture‐precipitation feedback , 2017 .
[134] R. DeFries,et al. Ecosystem management as a wicked problem , 2017, Science.
[135] Jun Li,et al. Social Media: New Perspectives to Improve Remote Sensing for Emergency Response , 2017, Proceedings of the IEEE.
[136] Hao Jiang,et al. Big Earth Data: a new challenge and opportunity for Digital Earth’s development , 2017, Int. J. Digit. Earth.
[137] Nagiza F. Samatova,et al. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data , 2016, IEEE Transactions on Knowledge and Data Engineering.
[138] A. P. Siebesma,et al. Climate goals and computing the future of clouds , 2017 .
[139] B. Fu,et al. Bidirectional coupling between the Earth and human systems is essential for modeling sustainability , 2016 .
[140] Willem Waegeman,et al. A non-linear Granger-causality framework to investigate climate–vegetation dynamics , 2016 .
[141] Tomoo Ushio,et al. “Big Data Assimilation” Revolutionizing Severe Weather Prediction , 2016 .
[142] Reiner Grundmann,et al. Climate change as a wicked social problem , 2016 .
[143] Elias Bareinboim,et al. Causal inference and the data-fusion problem , 2016, Proceedings of the National Academy of Sciences.
[144] Alan L. Porter,et al. How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China , 2016, PloS one.
[145] Jonathan F. Donges,et al. Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation , 2016 .
[146] Andrew J. Evans,et al. Dynamic calibration of agent-based models using data assimilation , 2016, Royal Society Open Science.
[147] Erik Schultes,et al. The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.
[148] J. Pearl,et al. Causal Counterfactual Theory for the Attribution of Weather and Climate-Related Events , 2016 .
[149] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[150] Amir Hossein Alavi,et al. Machine learning in geosciences and remote sensing , 2016 .
[151] Veronika Eyring,et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .
[152] Jürgen Kurths,et al. Identifying causal gateways and mediators in complex spatio-temporal systems , 2015, Nature Communications.
[153] Martin Krzywinski,et al. Points of Significance: Association, correlation and causation , 2015, Nature Methods.
[154] Jack J. Dongarra,et al. Exascale computing and big data , 2015, Commun. ACM.
[155] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[156] Chaogui Kang,et al. Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .
[157] Xin Li,et al. Integrated research methods in watershed science , 2015, Science China Earth Sciences.
[158] M. Scheffer,et al. Causal feedbacks in climate change , 2015 .
[159] M. Maslin,et al. Defining the Anthropocene , 2015, Nature.
[160] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[161] Shunlin Liang,et al. Observational evidence for impacts of vegetation change on local surface climate over northern China using the Granger causality test , 2015 .
[162] C. Mass,et al. Surface Pressure Observations from Smartphones: A Potential Revolution for High-Resolution Weather Prediction? , 2014 .
[163] Toshiyuki Imamura,et al. The 10,240‐member ensemble Kalman filtering with an intermediate AGCM , 2014 .
[164] Alex Pentland,et al. Sensing, Understanding, and Shaping Social Behavior , 2014, IEEE Transactions on Computational Social Systems.
[165] R. Kitchin,et al. Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..
[166] Han Liu,et al. Challenges of Big Data Analysis. , 2013, National science review.
[167] O. Korup,et al. Landslide prediction from machine learning , 2014 .
[168] Mark Graham,et al. Geography and the future of big data, big data and the future of geography , 2013 .
[169] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[170] George Sugihara,et al. Detecting Causality in Complex Ecosystems , 2012, Science.
[171] Li An,et al. Modeling human decisions in coupled human and natural systems: Review of agent-based models , 2012 .
[172] Mark Gahegan,et al. Geospatial Cyberinfrastructure: Past, present and future , 2010, Comput. Environ. Urban Syst..
[173] Tony Hey,et al. The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .
[174] F. Chapin,et al. A safe operating space for humanity , 2009, Nature.
[175] Elinor Ostrom,et al. Complexity of Coupled Human and Natural Systems , 2007, Science.
[176] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[177] P. Crutzen. Albedo Enhancement by Stratospheric Sulfur Injections: A Contribution to Resolve a Policy Dilemma? , 2006 .
[178] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[179] Edward N. Lorenz,et al. Designing Chaotic Models , 2005 .
[180] D. Rubin. Causal Inference Using Potential Outcomes , 2005 .
[181] Robert K. Kaufmann,et al. Investigating soil moisture feedbacks on precipitation with tests of Granger causality , 2002 .
[182] David William Keith. Geoengineering , 2021, Nature.
[183] W. Oechel,et al. FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .
[184] H. J. Schellnhuber,et al. ‘Earth system’ analysis and the second Copernican revolution , 1999, Nature.
[185] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[186] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[187] J. Aldrich. Correlations Genuine and Spurious in Pearson and Yule , 1995 .
[188] J. Wallace,et al. Teleconnections in the Geopotential Height Field during the Northern Hemisphere Winter , 1981 .
[189] H. Rittel,et al. Dilemmas in a general theory of planning , 1973 .