Nothing Special   »   [go: up one dir, main page]

Hähnel et al., 2020 - Google Patents

Using deep learning to extend the range of air pollution monitoring and forecasting

Hähnel et al., 2020

View PDF
Document ID
6578320569543228407
Author
Hähnel P
Mareček J
Monteil J
O'Donncha F
Publication year
Publication venue
Journal of Computational Physics

External Links

Snippet

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Hähnel et al. Using deep learning to extend the range of air pollution monitoring and forecasting
Nutkiewicz et al. Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach
CN116052427B (en) Inter-city inter-regional mobility prediction method and device based on private car travel track data
Leer et al. Fast flow field estimation for various applications with a universally applicable machine learning concept
Sun et al. Applications of physics-informed scientific machine learning in subsurface science: A survey
Singh et al. Artificial Neural Network on Graphical Processing Unit and its emphasis on ground water level prediction
Li et al. A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers
CN117520768A (en) Groundwater pollution prediction method of coupling space-time diagram convolution network and mechanism model
Paraskevas et al. Use of artificial neural network for spatial rainfall analysis
Tran et al. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Lin et al. Towards efficient and accurate CO2 sequestration optimization: Integrating hierarchical spatio-temporal information into deep-learning-based surrogate models
Sahoo et al. Application of hybrid MLP-GWO for monthly rainfall forecasting in Cachar, Assam: a case study
Mei et al. Coupling SWAT and LSTM for improving daily streamflow simulation in a humid and semi-humid river basin
Qu et al. Improving parking occupancy prediction in poor data conditions through customization and learning to learn
Tangrand Some new contributions to neural networks and wavelets with applications
Rojek Hybrid neural networks as prediction models
Kiavarz et al. ROOM-BASED energy demand classification of BIM data using graph supervised learning
Ouyang et al. Dual-channel spatial–temporal difference graph neural network for PM 2.5 forecasting
Edeling et al. Towards data-driven dynamic surrogate models for ocean flow
Wu et al. Hierarchical Bayesian network based incremental model for flood prediction
Su et al. Graph ode recurrent neural networks for traffic flow forecasting
Atamanyuk et al. Models and algorithms for prediction of electrical energy consumption based on canonical expansions of random sequences
Forghani et al. Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences
Achite et al. Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)
Ying et al. Research on town ecological landscape planning and governance based on fuzzy optimization method of Internet of things technology