Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation

This study proposes a deep neural network (DNN) as a downscaling framework with nonlinear features extracted by kernel principal component analysis (KPCA). KPCA utilizes kernel functions to extract nonlinear features from the source climatic data, reducing dimensionality and denoising. DNN is used to learn the nonlinear and complex relationships among the features extracted by KPCA to predict future regional rainfall patterns and trends in complex island terrain in Taiwan. This study takes Taichung and Hualien, on both the eastern and western sides of Taiwan’s Central Mountain Range, as examples to investigate the future rainfall trends and corresponding uncertainties, providing a reference for water resource management and usage. Since the Water Resources Agency (WRA) of the Ministry of Economic Affairs of Taiwan currently recommends the CMIP5 (AR5) GCM models for Taiwan regional climate assessments, the different emission scenarios (RCP 4.5, RCP 8.5) data simulated by two AR5 GCMs, ACCESS and CSMK3, of the IPCC, and monthly rainfall data of case regions from January 1950 to December 2005 in the Central Weather Administration (CWA) in Taiwan are employed. DNN model parameters are optimized based on historical scenarios to estimate the trends and uncertainties of future monthly rainfall in the case regions. The simulated results show that the probability of rainfall increase will improve in the dry season and will reduce in the wet season in the mid-term to long-term. The future wet season rainfall in Hualien has the highest variability. It ranges from 201 mm to 300 mm, with representative concentration pathways RCP 4.5 much higher than RCP 8.5. The median percentage increase and decrease in RCP 8.5 are higher than in RCP 4.5. This indicates that RCP 8.5 has a greater impact on future monthly rainfall.