ArticleScientific reports2024
Monthly climate prediction using deep convolutional neural network and long short-term memory.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
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- A hybrid artificial neural network-random forest model for enhanced wet bulb temperature prediction in arid urban environments.Scientific reports · 2026Article
- Machine and deep learning-based forecast of Tehran's accelerating nighttime surface heat island crisis.Scientific reports · 2026Article
- High resolution temperature forecasting using functional time series decomposition and advanced predictive models.Scientific reports · 2026Article
- Global temperature anomaly prediction by using additive twin LSTM networks.Scientific reports · 2026Article
- Enhanced deep learning networks integrated by fractals for air quality index analysis.Frontiers in artificial intelligence · 2026Article
- Beyond the 1.5°C planetary boundary: AI-forecast of Global Disease Burden and Health inequities.PloS one · 2026Article
- Article
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- Evaluation of climate prediction models in Yunnan, China: traditional methods and AI approaches.Scientific reports · 2025Article
- Multi-family wavelet-based feature engineering method for short-term time series forecasting.Scientific reports · 2025Article
- Accurate AQI forecasting in a high-altitude city using a simulated CVOCA-BiLSTM hybrid model: a case study of Lhasa, Tibet.Scientific reports · 2025Article
- Predicting Extreme Environmental Values with Hybrid Models: A Perspective Across Air Quality, Wind Energy, and Sensor Networks.Sensors (Basel, Switzerland) · 2025Article
- Deep transfer learning and attention based P2.5 forecasting in Delhi using a decade of winter season data.Scientific reports · 2025Article
- Physics-Informed Directed Graph Network-Based Temperature Forecasting Model.Sensors (Basel, Switzerland) · 2025Article
- Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study.BMC public health · 2025Article
- Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy.Scientific reports · 2025Article
- Transformer based models with hierarchical graph representations for enhanced climate forecasting.Scientific reports · 2025Article
- Deep learning framework for hourly air pollutants forecasting using encoding cyclical features across multiple monitoring sites in Beijing.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.