Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

House Price Prediction Using LSTM

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squar...

Prediction of Air Pollution Level Using System Identification Methods

For the prediction of the air pollution level, methods used in system identification can be used. In this case, the system will usually have a number of input parameters (mainly meteorological), and one output parameter, the pollutant concentration. For the pollutant generation and dispersion process, a model is to be developed that satisfies the experimental data (process parameters). This mod...

Air pollution prediction with statistical algorithms

Air pollution prediction via multi-label classification

A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classi...

Air Quality Index Prediction using K-Nearest Neighbor Technique

One of the classical data mining techniques is k-nearest neighbor. This method uses the class of the k nearest neighbor to classify a new instance. The distance is calculated with one of the multiple mathematical distance metrics. In this paper, the technique is used in the air quality forecast domain in order to predict the value of the air quality index. This index is used to categorize the p...