Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network.

In the last decades, air pollution has been a critical environmental issue, especially in developing countries like China. The governments and scholars have spent lots of effort on controlling air pollution and mitigating its impacts on human society. Accurate prediction of air quality can provide essential decision-making supports, and therefore, scholars have proposed various kinds of models and methods for air quality forecastings, such as statistical methods, machine learning methods, and deep learning methods. Deep learning-based networks, such as RNN and LSTM, have been reported to achieve good performance in recent studies. However, the excellent performance of these methods requires sufficient data to train the model. For stations that lack data, such as newly built monitoring stations, the performance of those methods is constrained. Therefore, a methodology that could address the data shortage problem in new stations should be explored. This study proposes a transfer learning-based stacked bidirectional long short term memory (TLS-BLSTM) network to predict air quality for the new stations that lack data. The proposed method integrates advanced deep learning techniques and transfer learning strategies to transfer the knowledge learned from existing air quality stations to new stations to boost forecasting. A case study in Anhui, China, was conducted to evaluate the effectiveness of TLS-BLSTM. The results show that the proposed method can help achieve 35.21% lower RMSE on average for the experimented three pollutants in new stations.

Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques.

To better control and manage harbor water quality is an important mission for coastal cities such as New York City (NYC). To achieve this, managers and governors need keep track of key quality indicators, such as temperature, pH, and dissolved oxygen. Among these, the Biochemical Oxygen Demand (BOD) over five days is a critical indicator that requires much time and effort to detect, causing great inconvenience in both academia and industry. Existing experimental and statistical methods cannot effectively solve the detection time problem or provide limited accuracy. Also, due to various human-made mistakes or facility issues, the data used for BOD detection and prediction contain many missing values, resulting in a sparse matrix. Few studies have addressed the sparse matrix problem while developing statistical detection methods. To address these gaps, we propose a deep learning based model that combines Deep Matrix Factorization (DMF) and Deep Neural Network (DNN). The model was able to solve the sparse matrix problem more intelligently and predict the BOD value more accurately. To test its effectiveness, we conducted a case study on the NYC harbor water, based on 32,323 water samples. The results showed that the proposed method achieved 11.54%-17.23% lower RMSE than conventional matrix completion methods, and 19.20%-25.16% lower RMSE than traditional machine learning algorithms.