Artificial Intelligence – Driven Climate Change Prediction And Sustainability Analysis Using Hybrid Machine Learning Models

Climate change refers to long-term alterations in temperature, precipitation, and environmental conditions caused primarily by human activities such as fossil fuel consumption and deforestation. AI provides powerful tools to analyze large-scale environmental data, identify patterns, and support decision-making.

AI techniques such as machine learning, deep learning, and remote sensing are widely used for:

• Climate prediction
• Carbon emission monitoring
• Renewable energy optimization
• Sustainable agriculture

Recent studies highlight that AI can contribute to climate mitigation, adaptation, and resilience building across sectors[6].

LITERATURE REVIEW

1. AI in Climate Change Mitigation

Research shows that AI models help in:

• Predicting weather and climate patterns
• Monitoring greenhouse gas emissions
• Optimizing energy grids

Machine learning techniques such as regression, neural networks, and satellite data analysis are commonly used for climate forecasting [4].

2. AI for Environmental Sustainability

AI contributes to sustainability by:

• Improving renewable energy systems
• Enhancing agricultural productivity
• Managing natural resources

Studies indicate AI’s role in achieving Sustainable Development Goals (SDGs) through environmental monitoring and policy support [5].

3. AI in Renewable Energy and Carbon Reduction

AI helps in:

• Predicting energy demand
• Improving grid efficiency
• Reducing carbon emissions

Empirical studies using multi-country datasets confirm that AI adoption is associated with reduced emissions and improved sustainability outcomes [10].

4. Research Gap

Despite advancements:

• Lack of explainable AI models
• Limited integration of multimodal environmental data
• High computational energy consumption

MATERIALS AND METHODS

1. Proposed System

We propose a Machine Learning-based Climate Prediction Model using:

• Input: Temperature, CO₂ levels, rainfall, humidity
• Output: Climate change prediction (temperature rise, drought risk)

2. System Architecture

Data Collection → Data Preprocessing → Feature Selection →

Model Training (Random Forest / LSTM) →

Prediction → Visualization

3. Dataset

• Historical climate datasets (temperature, rainfall)
• CO₂ emission data
• Satellite environmental data

Large-scale datasets such as climate simulation datasets are commonly used for ML-based climate modelling[8].

4. Algorithms Used

• Linear Regression
• Random Forest
• LSTM (Deep Learning)

5. Evaluation Metrics

• Accuracy
• Mean Squared Error (MSE)
• R² Score

PROPOSED MODEL DIAGRAM

Figure 1: AI-based Climate Prediction Framework

RESULTS

Sample Experimental Results

Figure 2: Model Performance Comparison

DISCUSSION

The results indicate that:

• Deep learning models (LSTM) outperform traditional models
• AI significantly improves climate prediction accuracy
• Random Forest provides a good balance between performance and interpretability

However:

• AI models require large datasets
• Energy consumption of AI systems can contribute to emissions
• Ethical concerns and bias must be addressed

AI is not a standalone solution but acts as a support tool for policymakers and environmental scientists [7].

APPLICATIONS

• Smart agriculture (crop prediction, irrigation planning)
• Disaster prediction (floods, droughts)
• Renewable energy optimization

Smart cities and resource management

CONCLUSION

AI has the potential to revolutionize climate change mitigation and sustainability efforts by providing predictive insights and optimizing resource utilization. The proposed model demonstrates improved accuracy in climate prediction, supporting proactive decision-making. Future work should focus on Explainable AI, energy-efficient models, and integration with IoT systems.

FUTURE WORK

• Integration with IoT-based environmental sensors
• Development of Green AI (low-energy models)

Multimodal AI (satellite + sensor + text data)

REFERENCES

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3. Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).
4. Bhim Shing,” AI for Climate Change Mitigation: A Review of Machine Learning Applicatios in Environmental Sustainability”. Transactions on Recent Developments inIndustrial IOT. Issue : Vol. 17 No. 17 (2025): TRIoT . Published 01/01/2025. 
5. Lucas Grief et al., “A Systematic Review of Current AI Techniques used in the Context of the SDGs”, International Journal of Environmental Research. Volume 19, Article number 1, (2025). 24th October 2024. https://doi.org/10.1007/s41742-024-00668-5
6. Muhammad Abdllah Safdar,”Artificial Intelligence for Environmental Sustainability: A Review”. Artificial Intelligence in Sustainability. July 2025
7. Rolnick et al., “ ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning”. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks. https://doi.org/10.48550/arXiv.2311.03721.
8. Sandiponi Tasha,” A Review of Artificial Intelligence Applications in Climate Change Mitigation”. International Journal of Environment and Climate Change, Volume 15, Issue 5, Page 443-449, 2025; Article No.IJEC.136233. ISSN: 2581-8627. DOI: https://doi.org/10.9734/ijecc/2025/v15i54864. Published 26/05/2025.
9. Samit Shivadekar,”AI for Climate Change and Sustainability”, Journal of Multidisciplinary Research and Innovation. E-ISSN – 3067-0977. June 2025. DOI:10.5281/zenodo.15716495
10. Zeeshan Khan et al., “ Harnessing Artificial Intelligence for Environmental Sustainability via Human Capital and Renewable Energy”. www.nature.com/scientificreports . (2025) 15:36739. https://doi.org/10.1038/s41598-025-20613-6.