Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water temperature

Abstract

Multi-step time series forecasting is essential in engineering. However, effective time series prediction of the agricultural environment is still a challenge due to the disturbance of external factors and the complexity of temporal patterns. The objective of this work is to provide an intelligent prediction system with field priori-knowledge for aquaculture facility thermal environment. Specifically, we present a novel network framework that considers the spatial correlation of exogenous environmental factors, the short-term and long-term temporal dependence of the sequence, and the spatio-temporal fusion correlation at different times. Among them, the dual-scale memory components focus on long-term seasonal and trend memory patterns as well as short-term fluctuation memory patterns, respectively. Besides, the attention mechanism coordinates the information flow between the long and short memory components. We applied the model to recirculating water temperature forecasting in the aquaponics greenhouse. The empirical results of global and local metrics indicate that our proposed model outperforms benchmark models. The RMSE of the proposed dual memory scale network (DMSNet) is 0.1631, 0.3206, and 0.3761, respectively, in the next 6 h, 12 h, and 24 h water temperature prediction. Ablation study proved that the components of our proposed network could be targeted to extract different information patterns from aquaponics environmental sequence data. Experimental results show that the thermal environment prediction based on DMSNet has higher precision in short-term and long-term forecasting multi-step prediction.