A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

Abstract

ABSTRACT:Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

“…Although this spatial impact is not common, it still brings great difficulties to the prediction of air quality. Using the RNN and LSTM model with 3-Nearest area's PM2.5 and weather data, the air quality prediction discover that DRNN pairs based on forward complement has the best prediction ability for time series and have good predictive and generalization abilities [14]. Based on the air quality data and meteorological data obtained by air quality monitoring stations and meteorological data monitoring stations in Beijing, Guo [15] obtained a method to predict the PM2.5 concentration of each air quality monitoring station in the next 48 hours by using the Tensorflow framework together with RNN, LSTM and GRU network and integrating the data of surrounding monitoring stations.…”

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

“…where x t is the input vector, h t is the hidden layer, y t is the experiment output vector, and W h is a weighted matrix. The RNN is applied to LSTM to create an environment for the computation process, obtain input, and create output [43]. During this process, long-term memory is created from short-term memory.…”

Forecasting air quality time series using deep learning

“…As mentioned previously, RNNs are well suited for multivariate time series data, with the ability to capture temporal dependencies over variable periods (Che et al 2016). RNNs have been used in many time-series applications, including speech recognition (Graves, Mohamed, and Hinton 2013), electricity load forecasting (Walid and Alamsyah 2017), and air pollution (Fan et al 2017;Gomez et al 2003). RNNs use the same basic building blocks as FFNNs with the addition of the output fed back into the input.…”