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- Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different VariablesPublication . Bai, Yun; Bezak, Nejc; Zeng, Bo; Li, Chuan; Sapac, Klaudija; Zhang, JinAccurate forecasts of daily runoff are essential for facilitating efficient resource planning and management of a hydrological system. In practice, daily runoff is needed for various practical applications and can be predicted using precipitation and evapotranspiration data. To this end, a long short-term memory (LSTM) under a cascade framework (C-LSTM) approach is proposed for forecasting daily runoff. This C-LSTM model is composed of a 2-level forecasting process. (1) In the first level, an LSTM is established to learn the relationship between the precipitation and evapotranspiration at present and to learn several meteorological variables one day in advance. (2) In the second level, an LSTM is constructed to forecast the daily runoff using the historical and simulated precipitation and evapotranspiration data produced by the first LSTM. Through cascade modeling, the complex features of the numerous targets in the different stages can be sufficiently extracted and learned by multiple models in a single framework. In order to evaluate the performance of the C-LSTM approach, four mesoscale sub-catchments of the Ljubljanica River in Slovenia were investigated. The results indicate that based on the root-mean-square error, the Pearson correlation coefficient, and the Nash-Sutcliffe model efficiency coefficient, the proposed model yields better results than two other tested models, including the normal LSTM and other neural network approaches. Based on the results of this study, we conclude that the LSTM under the cascade architecture is a valuable approach and can be regarded as a promising model for forecasting daily runoff.
- A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledgePublication . Bai, Yun; Xie, Jingjing; Wang, Dongqiang; Zhang, Wanjuan; Li, ChuanManufacturing quality prediction is one of the significant concerns in modern enterprise production management, which provides data support for reliability assessment and parameter optimization, thus improving the intelligent management level of enterprises and helping achieve high-quality products at lower costs. In this paper, an ensemble learning framework using rough knowledge is proposed for manufacturing quality prediction. The proposed model consists of three elements: (1) significant parameters in different production stages are selected based on attribute reduction and decision rule extraction of rough set theory (RS), (2) long short-term memory network (LSTM) is utilized for building the relationship between the significant parameters and manufacturing quality, and (3) the learning performance of the LSTM is reinforced by AdaBoost approach. To estimate the effectiveness of the proposed model, a competition dataset about manufacturing quality control is applied and six models are investigated. The comparison experiments show that the proposed model overwhelms all the comparison models in terms of root-mean-square error, threshold statistics and residuals analysis. In addition, the proposed model has statistically significant difference from all the comparative models. It is recommended from this work that the ensemble learning technique integrating the rough knowledge synchronously improves the sensitivity and regression capacity of the model.
- Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variantsPublication . Bai, Yun; Xie, Jingjing; Liu, Chao; Tao, Ying; Zeng, Bo; Li, ChuanEffective electricity consumption forecasting is extremely significant for enterprises' electricity planning which can provide data support for production decision, thus improving the level of enterprises' clean production. In recent years, recurrent neural network (RNN) and its variants have led to extensive research for time series forecasting. However, the performance and selection of these models in enterprise electricity forecasting have not been reported. With this study, we attempted to back some of these solutions with experimental results. This paper focused on a comparison for daily enterprise electricity consumption forecasting using different RNN models, i.e, standard RNN, long short-term memory-based RNN (LSTM), and gated recurrent unit-based RNN (GRU). To test their regression performance, three Chinese enterprises with different scales of electricity consumption are investigated. The comparison results show that the LSTM and the GRU models are slightly better than that of the RNN in terms of normalized root-mean-square error, mean absolute percentage error and threshold statistic. Moreover, the GRU model with the simplest structure is significantly different from the RNN, but not from LSTM in terms of Friedman testing. Hence the GRU model can be regarded as the first candidate for the enterprise electricity consumption forecasting in the future work.