Artificial intelligence helps us to fight against climate change and to preserve the environment in several ways.

Here you can find some worked examples and application problems on using these techniques in the environmental sector using Neural Designer.

Biodiversity and ecology

Machine learning enables scientists to analyze biological data more effectively, thereby enhancing their understanding of species, ecosystems, and environmental health.

By identifying patterns in plants, animals, and forests, these models support conservation efforts and ecological monitoring.

Classify iris flowers using machine learning

Learn how Neural Designer uses machine learning to classify iris flowers, offering a hands-on example for improving classification models and data analysis skills.
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Classify Palmer penguins using machine learning

Neural Designer uses machine learning for Palmer Penguins classification, supporting researchers in identifying species, improving data accuracy, and advancing environmental studies.
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Detect tree wilt with machine learning

Detect tree wilt early with Neural Designer, using machine learning to analyze environmental data, identify disease patterns, and support more effective forestry management.
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Pollution and emissions

Machine learning models can predict and monitor pollution levels, helping to reduce harmful emissions from industries, vehicles, and other sources.

These tools support cleaner technologies and more sustainable environmental policies.

Reduce emissions from a power plant with ML

Reduce harmful industrial emissions with Neural Designer, which uses machine learning to model NOx output in power plants, analyze data, and optimize combustion, helping industries cut pollutants, meet strict regulations, and improve long-term sustainability.
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Model water toxicity using machine learning

Minimize environmental hazards with Neural Designer, applying advanced machine learning to aquatic toxicity data to identify potential risks, optimize safety protocols, and ensure safer chemical practices in industries, ultimately protecting ecosystems and public health
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Estimate car emissions using machine learning

Reduce CO2 emissions with Neural Designer, using advanced machine learning to model car emissions, predict fuel efficiency, optimize vehicle performance, and support the development of more sustainable automotive technologies.
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Climate and natural hazards

Machine learning is applied to forecast and assess risks associated with natural hazards, such as forest fires.

These predictive models enhance prevention strategies and facilitate rapid responses to protect ecosystems and human communities.

Predict forest fires using machine learning

Neural Designer applies machine learning to predict forest fires, analyzing a wide range of environmental data to identify risks, forecast fire behavior, optimize prevention efforts, and improve emergency response strategies.
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Conclusions

Machine learning provides valuable tools to tackle environmental challenges.

From monitoring biodiversity to reducing emissions and predicting natural hazards, these models help researchers and policymakers make better decisions.

As data availability increases, their role in building a more sustainable future will only grow.