AIMC Topic: Climate Change

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Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food...

Amazon's climate crossroads: analyzing air pollution and health impacts under machine learning-based temperature increase scenarios in Northern Mato Grosso, Brazil.

Environmental geochemistry and health
Air pollution has long been a public health concern in South America, now increasingly linked to climate change. In Brazil, this issue is particularly acute in smaller cities with limited monitoring infrastructure. Sinop, located in the Amazon biome ...

Snow Height Sensors Reveal Phenological Advance in Alpine Grasslands.

Global change biology
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change-induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions...

Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models.

PloS one
Accurate monthly runoff forecasting is vital for water management, flood control, hydropower, and irrigation. In glacierized catchments affected by climate change, runoff is influenced by complex hydrological processes, making precise forecasting eve...

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions.

Integrative and comparative biology
In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and ...

[Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning].

Huan jing ke xue= Huanjing kexue
The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors o...

Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

Radiology
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressi...

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective.

Water science and technology : a journal of the International Association on Water Pollution Research
This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine ...

Deep learning based an effective hybrid model for water quality assessment.

Water environment research : a research publication of the Water Environment Federation
Water, which is very important for life and civilizations on Earth, has been a source of life for all living things. However, freshwater resources gradually decrease due to climate change, pollution, and population growth. Water pollution is the qual...