International Journal of Intelligence Science

International Journal of Intelligence Science

ISSN Print: 2163-0283
ISSN Online: 2163-0356
www.scirp.org/journal/ijis
E-mail: ijis@scirp.org
"Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach"
written by Artemio Sotomayor-Olmedo, Marco A. Aceves-Fernández, Efrén Gorrostieta-Hurtado, Carlos Pedraza-Ortega, Juan M. Ramos-Arreguín, J. Emilio Vargas-Soto,
published by International Journal of Intelligence Science, Vol.3 No.3, 2013
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] A Comparative and Systematic Study of Machine Learning (ML) Approaches for Particulate Matter (PM) Prediction
Archives of Computational …, 2024
[2] Research on the usability of different machine learning methods in visibility forecasting
Atmósfera, 2023
[3] Estimation and analysis of missing temperature data in high altitude and snow-dominated regions using various machine learning methods
Environmental Monitoring and Assessment, 2023
[4] Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR …
Geoscientific Model …, 2022
[5] PM10 Parametresinin Makine Öğrenmesi Algoritmalari ile Mekânsal Analizi, Kayseri İli Örneği
Dokuz Eylül üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 2022
[6] Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods
, 2022
[7] Indoor Air Pollution Forecasting Using Deep Neural Networks
Astorga, IA Santiago-Castillejos… - Mexican Conference on …, 2022
[8] Use of Biomass Fuels for Cooking and Improved Biomass Stoves in Mexico
Sustainable Policies and …, 2022
[9] A decomposition-ensemble broad learning system for AQI forecasting
Neural Computing and …, 2022
[10] The Role of GARCH Effect on the Prediction of Air Pollution
Sustainability, 2022
[11] Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter
Monge, MA Aceves-Fernández… - Journal of the Air & …, 2022
[12] Airborne Particulate Matter Modeling: A Comparison of Three Methods Using a Topology Performance Approach
Montañez, MA Aceves-Fernández… - Applied Sciences, 2021
[13] Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter
Sensors, 2021
[14] Intelligent modeling strategies for forecasting air quality time series: A review
2021
[15] Air Quality Classification Using Support Vector Machine
2021
[16] PRARANCANGAN PABRIK KIMIA METIL ASETAT DARI ASAM ASETAT DAN METANOL KAPASITAS 25.000 TON/TAHUN
2021
[17] Pollution and climate changes. Feedback from space for sustainable cities and communities
2021
[18] Pathway and Future of IoE in Smart Cities: Challenges of Big Data and Energy Sustainability
2021
[19] Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021
[20] BOD5 Prediction Using machine learning methods
2021
[21] Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives
2021
[22] The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2. 5 Concentration in China?
2020
[23] Chemometrics for environmental monitoring: a review
2020
[24] Source number estimation based on a novel multi-view meta-hierarchical classification framework
2020
[25] A Machine Learning Approach to Predict Air Quality in California
2020
[26] Research Article A Machine Learning Approach to Predict Air Quality in California
2020
[27] Fundamental research
continuum (BICs), 2020
[28] Propuesta de red neuronal convolutiva para la predicción de partículas contaminantes PM10
2019
[29] Propuesta de red neuronal convolutiva para la predicción de partículas contaminantes PM10.
2019
[30] Support Vector Regression for Time-Series
2019
[31] An ensemble-based model of PM2. 5 concentration across the contiguous United States with high spatiotemporal resolution
2019
[32] Support Vector Machine (SVM) aggregation modelling for spatio-temporal air pollution analysis
2019
[33] Support vector regression for time-series: a machine learning approach to predict the air quality
2019
[34] Application of statistical techniques in environmental modelling
2019
[35] Multi-Level Air Quality Classification in China Using Information Gain and Support Vector Machine Hybrid Model.
2019
[36] Predictive analytics of PM10 concentration levels using detailed traffic data
2019
[37] Air Pollution Prediction Using Machine Learning
2019
[38] The Hybrid Neural Networks-ARIMA/X Models and ANFIS Model for PM-10 Forecasting: A Case Study of Chiang Mai, Thailand's High Season
2018
[39] Multi-Level Air Quality Classification in China Using Information Gain and Support Vector Machine
2018
[40] The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali.
Telkomnika, 2018
[41] Improving air quality management using gradient boosting based hierarchical temporal memory neural networks and fuzzy based classification based …
2018
[42] The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali
2018
[43] Implementación de un modelo del comportamiento de los niveles de concentración de PM10 utilizando herramientas de aprendizaje de máquina en la ciudad …
2017
[44] Implementación de un modelo del comportamiento de los niveles de concentración de PM10 utilizando herramientas de aprendizaje de máquina en la ciudad de …
2017
[45] Classification trees and PM10 dynamics in Bogotá, Colombia
2017
[46] A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States
Environmental Research, 2017
[47] ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR POLLUTION ANALYSIS
International Journal of Data Mining & Knowledge Management Process, 2017
[48] Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies
Atmosphere, 2016
[49] Using geosocial search for urban air pollution monitoring
Pervasive and Mobile Computing, 2016
[50] Statistical Modeling Approaches for PM10 Prediction in Urban Areas
2016
[51] Pervasive and Mobile Computing
2016
[52] Modelling atmospheric ozone concentration using machine learning algorithms
2016
[53] Enhancement of a Neuro-Fuzzy Models Using Ant Colony Optimization for the Prediction level of CO Pollution
2015
[54] Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and …
Remote Sensing of Environment, 2015
[55] Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and …
Remote Sensing of Environment, 2015
[56] Prediction models for ozone in metropolitan area of Mexico City based on artificial intelligence techniques
International Journal of Information and Decision Sciences, 2015
[57] Using wavelet–feedforward neural networks to improve air pollution forecasting in urban environments
Environmental monitoring and assessment, 2015
[58] Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms
International Journal of Intelligence Science, 2014
[59] Implementación de un modelo del comportamiento de los niveles de concentración de PM10 utilizando herramientas de aprendizaje de máquina en la …
[60] An Integrated Approach for Immediate and Long-Term Air Quality Regulation and Monitoring in Mexico
Free SCIRP Newsletters
Copyright © 2006-2025 Scientific Research Publishing Inc. All Rights Reserved.
Top