Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

Abstract

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.

Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

“…To better analyze the forecasting performance of the deep LSTM_NN, four methods were selected for comparisons, namely RF [38], deep neural network (DNN) [18], RNN [20], and GRU [39]. We have discussed the optimized combination of the endogenous input variables using RF in our previous study [26], and the impacts of added weather variables on passenger flow forecasting have been further studied.…”

“…The most widely used input variables are historical passenger flow data and their corresponding spatiotemporal data. Although experimental results show that weather data [4,5] were able to represent the passenger recurrent neural network (RNN) [19], long short-term memory neural network (LSTM_NN) [20], and gate recurrent unit neural network (GRU_NN) [21], have been comprehensively applied to traffic flow forecasting. An RNN-based microscopic car-following model was developed to capture and accurately predict traffic oscillation in Emeryville, California [22].…”

A Spatial Analysis Methodology Based on Lazy Ensembled Adaptive Associative Classifier and GIS For Examining the Influential Factors on Traffic Fatalities

“…In data analysis, AI-related methods can be divided into machine learning methods and deep learning methods [16], [17]. Deep learning methods have caught lots of attention these years in traffic analysis due to its superb nonlinear modeling ability [18], [19]. However, its black box nature restricts it from analyzing impact factors [20].…”