Air quality prediction in smart cities using machine learning technologies based on sensor data: a review
The influence of machine learning technologies is rapidly increasing and penetrating almost
in every field, and air pollution prediction is not being excluded from those fields. This paper …
in every field, and air pollution prediction is not being excluded from those fields. This paper …
Time series prediction using support vector machines: a survey
NI Sapankevych, R Sankar - IEEE computational intelligence …, 2009 - ieeexplore.ieee.org
Time series prediction techniques have been used in many real-world applications such as
financial market prediction, electric utility load forecasting, weather and environmental state …
financial market prediction, electric utility load forecasting, weather and environmental state …
Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging
NO2 is a combustion byproduct that has been associated with multiple adverse health
outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble …
outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble …
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network …
J Adamowski, H Fung Chan, SO Prasher… - Water Resources …, 2012 - Wiley Online Library
Daily water demand forecasts are an important component of cost‐effective and sustainable
management and optimization of urban water supply systems. In this study, a method based …
management and optimization of urban water supply systems. In this study, a method based …
Support vector machines with simulated annealing algorithms in electricity load forecasting
Accurate forecasting of electricity load has been one of the most important issues in the
electricity industry. Recently, along with power system privatization and deregulation …
electricity industry. Recently, along with power system privatization and deregulation …
Air pollution forecasting based on attention‐based LSTM neural network and ensemble learning
DR Liu, SJ Lee, Y Huang, CJ Chiu - Expert Systems, 2020 - Wiley Online Library
With air pollution having become a global concern, scientists are committed to working on its
amelioration. In the field of air pollution prediction, there have been good results in …
amelioration. In the field of air pollution prediction, there have been good results in …
Online prediction model based on support vector machine
For time-series forecasting problems, there have been several prediction models to data, but
the development of a more accurate model is very difficult because of high non-linear and …
the development of a more accurate model is very difficult because of high non-linear and …
Forecasting air quality in kiev during 2022 military conflict using sentinel 5P and optimized machine learning
Recent studies have demonstrated that the Ukraine–Russia war has incurred evident
changes to anthropogenic activities in the Kiev metropolis. Hence, this work employs …
changes to anthropogenic activities in the Kiev metropolis. Hence, this work employs …
A feature selection and multi-model fusion-based approach of predicting air quality
Y Zhang, R Zhang, Q Ma, Y Wang, Q Wang, Z Huang… - ISA transactions, 2020 - Elsevier
With the rapid development of China's industrialization, the air pollution is becoming more
and more serious. It is vital for us to predict the air quality for determining the further …
and more serious. It is vital for us to predict the air quality for determining the further …
Prediction-based delay optimization data collection algorithm for underwater acoustic sensor networks
The past years have seen a rapid development of autonomous underwater vehicle-aided
(AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The …
(AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The …