Application of machine learning algorithms in multimodal integration of precipitation and temperature

Using six multimodal integration methods, including arithmetic averaging, weighted averaging, multiple linear regression, BP neural network, long-short-term memory (LSTM) neural network, and random forest (RF), this study integrated five global climate models (GCMs) data in CMIP6, and based on historical precipitation and temperature data of the Yellow River Basin water conservation region, the simulation performance of different integration methods were evaluated. The multimodal integration method with the best performance was selected to predict future precipitation and temperature under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show that the multimodal integration could well reproduce the variations of historical precipitation and temperature, and the LSTM neural network method has the best performance. In three scenarios, future average annual precipitation all increases, but the change of seasonal precipitation in the future varies. Under the SSP1-2.6 scenario, the annual precipitation peaks occur at the beginning of each period, while annual precipitation increases in the near term and decreases obviously in the long term under the SSP2-4.5 and SSP5-8.5 scenarios. Future temperature in three scenarios shows upward trends of different degrees, and the amplitude and rate of temperature increase from small to large are: SSP1-2.6, SSP2-4.5, SSP5-8.5. Future temperature increases greatest in autumn and least in winter. There are large uncertainties in the future precipitation and temperature predicted by multimodal integration methods, and the uncertainty in the medium to long term is greater than in the short term. The uncertainty in future precipitation projection is relatively greater than that of temperature, and the uncertainty in autumn and winter is significantly greater than that in spring and summer.