AIMC Topic: Forests

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Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.

Microbiology spectrum
Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to...

Forest fire detection system using wireless sensor networks and machine learning.

Scientific reports
Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher perc...

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

Computers in biology and medicine
BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order ...

Identifying of Quercus vulcanica and Q. frainetto growing in different environments through deep learning analysis.

Environmental monitoring and assessment
Quercus is one of the important elements of forests worldwide. But the diagnosis of the species in this genus in particular using leaves is pretty challenging due to the presence of natural hybrids and phenotypically plastic trait expression. In this...

Extreme fire weather is the major driver of severe bushfires in southeast Australia.

Science bulletin
In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019-2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast ...

The Effect of Synergistic Approaches of Features and Ensemble Learning Algorith on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8.

Sensors (Basel, Switzerland)
Although the combination of Airborne Laser Scanning (ALS) data and optical imagery and machine learning algorithms were proved to improve the estimation of aboveground biomass (AGB), the synergistic approaches of different data and ensemble learning ...

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

PloS one
Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change...

Prediction of soil-bearing capacity on forest roads by statistical approaches.

Environmental monitoring and assessment
The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks ...

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

PloS one
Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Fores...

Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

PloS one
Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological netwo...