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

Bozdağ et al., 2020 - Google Patents

Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey

Bozdağ et al., 2020

View PDF
Document ID
8270393580561575919
Author
Bozdağ A
Dokuz Y
Gökçek �
Publication year
Publication venue
Environmental Pollution

External Links

Snippet

With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • 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
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • 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
    • 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
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/0004Gaseous mixtures, e.g. polluted air

Similar Documents

Publication Publication Date Title
Bozdağ et al. Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
Murillo-Escobar et al. Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia
Suleiman et al. Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2. 5)
Yafouz et al. Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms
Tella et al. Spatial assessment of PM10 hotspots using random forest, K-nearest neighbour and Naïve Bayes
Khedairia et al. Impact of clustered meteorological parameters on air pollutants concentrations in the region of Annaba, Algeria
Durao et al. Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models
Elbaz et al. Spatiotemporal air quality forecasting and health risk assessment over smart city of NEOM
Lim et al. Air pollution matter prediction using recurrent neural networks with sequential data
Domańska et al. Explorative forecasting of air pollution
Persis et al. Predictive modeling and analysis of air quality–Visualizing before and during COVID-19 scenarios
Yadav et al. Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review
Jamal et al. Predicting air quality index based on meteorological data: A comparison of regression analysis, artificial neural networks and decision tree
Zhalehdoost et al. A review of the application of machine learning and geospatial analysis methods in air pollution prediction
Cao How to better predict the effect of urban traffic and weather on air pollution? norwegian evidence from machine learning approaches
Alloghani Harnessing AI for sustainability: applied AI and machine learning algorithms for air quality prediction
Panneerselvam et al. ACBiGRU-DAO: attention convolutional bidirectional gated recurrent unit-based dynamic arithmetic optimization for air quality prediction
Ramadhani et al. Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN
Saritha et al. Determination of crisis on climatic fluctuations and smog deterioration by categorizing the condition using predictive analytics
Srivastava et al. Performance Analysis of Machine Learning Models for Air Pollution Prediction
Mahmood et al. Recommender system for ground-level Ozone predictions in Kuwait
Jayaraj Air quality monitoring and disease prediction using IoT and machine learning
Yahya Studying the Global Climate Changes using Artificial Intelligence: An Overview
Iskandaryan et al. Application of deep learning and machine learning in air quality modeling
Arampongsanuwat et al. Pm10 prediction model by support vector regression based on particle swarm optimization