Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

Read more about AI can’t deliver climate gains without strong governance and capacity building on Devdiscourse ...

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include ...

A new study has shown that biochar, a carbon-rich material produced from biomass, can significantly reduce phosphorus losses ...

Scientists at the European Centre for Medium-Range Weather Forecasts have unveiled a machine learning technique that pinpoints optimal locations for tree planting, offering a powerful tool for climate ...

Zoonova AI today announced the launch of Alpha AI, a new investing platform designed to make advanced market intelligence more accessible through a natural-language AI Command Center. Alpha AI ...

A UC Berkeley team used Apache Spark ML to predict airline delays at scale, training models on millions of flight records and ...

Using six gut- and diet-derived metabolites, a machine learning model had 79% accuracy in classifying adults as having ...

A new soil-moisture retrieval strategy has improved the accuracy of satellite-based moisture mapping by combining microwave reflection signals with vegetation-structure information that conventional ...