Abstract
In outdoor environments, fluctuating airflow and gas distribution make gas source localization (GSL) tasks difficult. In our research, we use neural networks (NNs) to overcome these difficulties by applying long short-term memory deep neural networks (LSTM-DNNs) to time series data taken from a gas sensor array and anemometer to estimate the position of a gas source. In this paper, we present NNs for GSL with the ability to use various length input data and estimate a gas source location each time-step. In doing so, we were able to estimate the location of a gas source within 40 time-steps (20 s) and achieved (using 300 time-steps) an estimation accuracy of 95%.
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