Abstract
Real-time monitoring and accurate prediction of toxic gas concentration in the future are of great significance for emergency capability assessment and rescue work. At present, the method of gas concentration prediction based on artificial intelligence still has problems of low accuracy, slow convergence speed and equal feature importance. This paper proposes a feature-aware LSTM model to predict pollutant gas concentration. First of all, we design a set of multi-component toxic gas monitoring equipment that applies in pollution environment, which can at the same time monitor CO, NO<sub>2</sub>, NH<sub>3</sub>, HCN, H<sub>2</sub>S and SO<sub>2</sub>, six common pollutants; To accurate estimate the toxic gas concentration, we combine the collected the gas data and the environmental parameters and regard them as the input features, and then we obtain toxic gas data based on the sampling policy and the environmental data as our data-set. Finally, we train a FA-LSTM gas concentration prediction model on these data-set. We test the proposed model and compared with ARIMA, ETS and BP network on the same test set. Experimental results show that the proposed model outperforms traditional concentration prediction model. Also, it is better than other state-of-the-art models in predicting accuracy.
Highlights
A contaminated site, known as a "brownfield", refers to the space environment that carries harmful substances due to accumulation, storage, treatment, disposal or other means, causing harm or potential risk to human health and the region [1]
The evaluation results show that the performance of our proposed model on the pollutant gas concentration prediction is obviously better than other methods
We used the trained long-term and short-term memory neural network (LSTM) network model, ARMIA linear model, exponential smoothing model (ETS) model, support vector machine (SVM) model and BP model to simultaneously predict the concentration of each component monitoring gas in the contaminated site on a certain day
Summary
A contaminated site, known as a "brownfield", refers to the space environment that carries harmful substances due to accumulation, storage, treatment, disposal or other means (such as migration), causing harm or potential risk to human health and the region [1]. In order to simplify the complexity of model building, a series of pollutant prediction models based on machine learning were proposed [9] Such methods have the limits of low accuracy, slow training speed [10], [11], or the model is difficult to converge and optimize [12]. The evaluation results show that the performance of our proposed model on the pollutant gas concentration prediction is obviously better than other methods. The contributions of this paper are as follows: (1) We novelly combined the monitoring concentration data collected in the field with environmental parameters to predict multi-gas pollutant concentrations; the effective combination of these features helps improve the accuracy of the model.
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