Monthly climate prediction using deep convolutional neural network and long short-term memory

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951-31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity,…

• NN
  National Natural Science Foundation of China

  Award: 41572150
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2023MD075
• SK
  State Key Laboratory of Loess and Quaternary Geology