[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wei et al., 2019 - Google Patents

Machine learning and statistical models for predicting indoor air quality

Wei et al., 2019

View PDF
Document ID
3282379469305562359
Author
Wei W
Ramalho O
Malingre L
Sivanantham S
Little J
Mandin C
Publication year
Publication venue
Indoor Air

External Links

Snippet

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Wei et al. Machine learning and statistical models for predicting indoor air quality
Azid et al. Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: A case study in Malaysia
Kapoor et al. Machine Learning‐Based CO2 Prediction for Office Room: A Pilot Study
Dutta et al. OccupancySense: Context-based indoor occupancy detection & prediction using CatBoost model
Dai et al. A recurrent neural network using historical data to predict time series indoor PM2. 5 concentrations for residential buildings
Czernecki et al. Assessment of machine learning algorithms in short-term forecasting of pm10 and pm2. 5 concentrations in selected polish agglomerations
Yang et al. Hurricane annual cycle controlled by both seeds and genesis probability
Kristiani et al. PM2. 5 forecasting model using a combination of deep learning and statistical feature selection
Lee et al. Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data
Kadiyala et al. Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus
Toharudin et al. Boosting Algorithm to Handle Unbalanced Classification of PM 2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM
Ali Shah et al. A novel phase space reconstruction‐(PSR‐) based predictive algorithm to forecast atmospheric particulate matter concentration
Sonawani et al. Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living
Saini et al. A novel application of fuzzy inference system optimized with particle swarm optimization and genetic algorithm for PM10 prediction
Nicolis et al. Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile
Arjomandnia et al. Renovating buildings by modelling energy–CO2 emissions using particle swarm optimization and artificial neural network (case study: Iran)
Nouri et al. Prediction of PM2. 5 concentrations using principal component analysis and artificial neural network techniques: A case study: Urmia, Iran
Kshirsagar et al. Anatomization of air quality prediction using neural networks, regression and hybrid models
Choudhary et al. Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants
Zhu et al. Predicting carbonaceous aerosols and identifying their source contribution with advanced approaches
Gabriel et al. LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors
French et al. Communicating geographical risks in crisis management: The need for research
Nguyen et al. Predicting the opening state of a group of windows in an open-plan office by using machine learning models
Bolla et al. Weather Forecasting Method from Sensor Transmitted Data for Smart Cities Using IoT
Saini et al. Modelling particulate matter using multivariate and multistep recurrent neural networks