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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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

Qingchun Guo^1,2,3,4, Zhenfang He^5,6, Zhaosheng Wang⁷

¹School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China. guoqingchun@lcu.edu.cn.

Scientific Reports

|July 31, 2024

Summary

This summary is machine-generated.

Artificial intelligence models, including the hybrid CNN-LSTM, forecast climate parameters for Jinan city. The CNN-LSTM model significantly improves accuracy in predicting monthly climate factors, aiding disaster prevention.

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Published on: September 25, 2021

Area of Science:

Environmental Science
Artificial Intelligence
Climate Modeling

Background:

Climate change poses significant threats to ecosystems, agriculture, and human well-being.
Accurate climate parameter forecasting is crucial for mitigation and adaptation strategies.
Jinan city's climate data spanning 72 years (1951-2022) provides a robust basis for analysis.

Purpose of the Study:

To evaluate the efficacy of various artificial intelligence (AI) models in simulating and forecasting monthly climate parameters.
To compare the predictive accuracy of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM model.
To identify the most effective AI model for precise climate forecasting in Jinan city.

Main Methods:

Utilized 72 years of monthly climate data (temperature, precipitation, humidity, sunlight hours) for Jinan city.
Employed time series data with 12-month delays as input for AI models.
Compared ANN, RNN, LSTM, CNN, and CNN-LSTM models using Mean Absolute Error, Root Mean Square Error (RMSE), and Correlation Coefficient (R).

Main Results:

The hybrid CNN-LSTM model demonstrated superior accuracy in forecasting monthly climate parameters compared to individual ANN, RNN, LSTM, and CNN models.
The CNN-LSTM model achieved the lowest RMSE for monthly average atmospheric temperature (0.6292 °C), significantly outperforming others.
The proposed models, particularly CNN-LSTM, show substantial potential for enhancing climate forecasting precision.

Conclusions:

The hybrid CNN-LSTM model offers a significant advancement in climate forecasting accuracy.
Improved climate prediction capabilities can bolster meteorological disaster prevention, flood control, and drought resistance efforts.
AI-driven climate simulations are vital for sustainable development and safeguarding human health against climate change impacts.