A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models
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Abstract
Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.Keywords
Funding Information
- Natural Science Foundation of Shandong Province (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
- LAC/CMA (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
- State Key Laboratory of Loess and Quaternary Geology Foundation (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
- Shandong Province Higher Educational Humanities and Social Science Program (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
- National Natural Science Foundation of China (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
- Junior Faculty Support Program for Scientific and Technological Innovations in Shandong Provincial Higher Education Institutions (ZR2023MD075, 2023B02, SKLLQG2211, J18RA196, 41572150, 2021KJ085)
This publication has 98 references indexed in Scilit:
- Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks ModelingWater Resources Management, 2024
- Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural NetworksWater Resources Management, 2024
- Daily air temperature forecasting using LSTM-CNN and GRU-CNN modelsActa Geophysica, 2023
- Attribution of runoff and hydrological drought changes in an ecologically vulnerable basin in semi‐arid regions of ChinaHydrological Processes, 2023
- Monthly precipitation prediction at regional scale using deep convolutional neural networksHydrological Processes, 2023
- Univariate Deep Learning models for prediction of daily average temperature and Relative Humidity: The case study of Chennai, IndiaJournal of Earth System Science, 2023
- Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenariosEnvironmental Science and Pollution Research, 2022
- Assessment of CMIP6 model performance for temperature and precipitation in Xinjiang, ChinaAtmospheric and Oceanic Science Letters, 2022
- Air temperature forecasting using artificial neural network for Ararat valleyEarth Science Informatics, 2021
- Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal DimensionsWater Resources Management, 2014