RSDF-AM-LSTM: Regional Scale Division Rainfall Forecasting Using Attention and LSTM

Abstract

Short-term rainfall forecasting plays a critical role in meteorology, hydrology, and other related areas. Currently, data-driven approaches have made considerable progress in rainfall forecasting. However, these approaches suffer from the following major drawbacks. First, they do not accommodate a reasonable and effective model to mathematically represent the complete changes of a rainfall block. Second, scale division rainfall forecasting is overlooked in existing literature. Third, generalization is not well validated in these approaches. To address these issues, we propose a novel Regional Scale Division Forecasting model using attention mechanism and long short term memory network (RSDF-AM-LSTM) for short-term and scale-division rainfall forecasting. The forecasting model can take full account of the regional characteristics of the rainfall blocks. It is established by employing a method similar to image evolution. The approach also provides a reasonable and effective model to formulate the complete changes of rainfall blocks, including newborn, splitting, strengthening, weakening, and merging. A deep learning algorithm based on AM-LSTM is proposed to forecast the change of rainfall block. AM-LSTM describes temporal and spatial association among four parallel LSTMs through AM. The rainfall data used in our experiments are obtained from 3,200 meteorological stations in and around China. The experimental results show that RSDF-AM-LSTM outperforms two popular atmospheric models and other traditional machine learning approaches.