A time series forecasting based multi-criteria methodology for air quality prediction

[1]  Majid Ahmadi,et al.  Efficient hardware implementation of the hyperbolic tangent sigmoid function , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[2]  Ola M. Surakhi,et al.  On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges , 2020 .

[3]  A. Masih,et al.  Machine learning algorithms in air quality modeling , 2019 .

[4]  Claudio Moraga,et al.  The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.

[5]  Suat Özdemir,et al.  A deep learning model for air quality prediction in smart cities , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[6]  Javier Del Ser,et al.  The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain , 2016 .

[7]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[8]  Carsten Maple,et al.  Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities , 2019, IEEE Access.

[9]  Shikha Gupta,et al.  Linear and nonlinear modeling approaches for urban air quality prediction. , 2012, The Science of the total environment.

[10]  Chih-Hung Wu,et al.  Air quality prediction by neuro-fuzzy modeling approach , 2020, Appl. Soft Comput..

[11]  Baowei Wang,et al.  An air quality forecasting model based on improved convnet and RNN , 2021, Soft Computing.

[12]  Derya Soydaner,et al.  A Comparison of Optimization Algorithms for Deep Learning , 2020, Int. J. Pattern Recognit. Artif. Intell..

[13]  Enrico Marzano,et al.  Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław , 2019, ADBIS.

[14]  Fang Liu,et al.  Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU , 2019, IEEE Access.

[15]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[16]  J. Kamińska,et al.  A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions. , 2019, The Science of the total environment.

[17]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[18]  Szymon Szewrański,et al.  Decision Support System in Public Transport Planning for Promoting Urban Adaptation to Climate Change , 2019, IOP Conference Series: Materials Science and Engineering.

[19]  Mohamed Elwekeil,et al.  Development of an Optimized Regression Model to Predict Blast-Driven Ground Vibrations , 2021, IEEE Access.

[20]  R. Vinayakumar,et al.  DeepAirNet: Applying Recurrent Networks for Air Quality Prediction , 2018 .

[21]  C. Tan,et al.  Monitoring of heat-induced carcinogenic compounds (3-monochloropropane-1,2-diol esters and glycidyl esters) in fries , 2020, Scientific Reports.

[22]  Saeid Baroutian,et al.  Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks , 2012 .

[23]  Yue-Shan Chang,et al.  Ensemble multifeatured deep learning models for air quality forecasting , 2021 .

[24]  Wei Xu,et al.  Spatial-temporal prediction of air quality based on recurrent neural networks , 2019, HICSS.

[25]  Arnaud Doucet,et al.  On the Impact of the Activation Function on Deep Neural Networks Training , 2019, ICML.

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[28]  Kıymet Kaya,et al.  Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting , 2020, Scientific Reports.

[29]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[30]  Zhongfei Zhang,et al.  Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality , 2017, IEEE Transactions on Knowledge and Data Engineering.

[31]  Graham W. Taylor,et al.  Forecasting air quality time series using deep learning , 2018, Journal of the Air & Waste Management Association.

[32]  Cyrus Shahabi,et al.  Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning , 2018, SIGSPATIAL/GIS.

[33]  H. Jaap van den Herik,et al.  Air Quality Forecast through Integrated Data Assimilation and Machine Learning , 2019, ICAART.

[34]  Qi Li,et al.  A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN , 2017 .

[36]  Weidong Zhang,et al.  Prediction of 24-hour-average PM(2.5) concentrations using a hidden Markov model with different emission distributions in Northern California. , 2013, The Science of the total environment.

[37]  Shi-Jinn Horng,et al.  Deep Air Quality Forecasting Using Hybrid Deep Learning Framework , 2018, IEEE Transactions on Knowledge and Data Engineering.

[38]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[39]  Fernando Jiménez,et al.  Simple Versus Composed Temporal Lag Regression with Feature Selection, with an Application to Air Quality Modeling , 2020, 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

[40]  Deep Learning Techniques for Air Pollution , 2021, 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS).

[41]  Anirban Mitra,et al.  Estimation of Air Quality Index from Seasonal Trends Using Deep Neural Network , 2018, ICANN.

[42]  Xianfeng Zhang,et al.  Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations , 2019, Aerosol and Air Quality Research.

[43]  Roy M. Harrison,et al.  Regression modelling of hourly NOx and NO2 concentrations in urban air in London , 1997 .

[44]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[45]  Sarbani Roy,et al.  Long-term time-series pollution forecast using statistical and deep learning methods , 2021, Neural Comput. Appl..

[46]  Jinchang Ren,et al.  Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM , 2020, Applied Sciences.

[47]  Mehmet Taştan,et al.  Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose , 2019, Applied Sciences.

[48]  Onur Avci,et al.  1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.