AIMC Topic: Climate Change
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Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.
Journal of environmental management
Jul 8, 2024
This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 a...
Machine learning in soil nutrient dynamics of alpine grasslands.
The Science of the total environment
Jun 25, 2024
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...
Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.
PloS one
Jun 25, 2024
Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecol...
Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.
Diagnostic and interventional imaging
Jun 24, 2024
The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raise...
Analyzing variation of water inflow to inland lakes under climate change: Integrating deep learning and time series data mining.
Environmental research
Jun 23, 2024
The alarming depletion of global inland lakes in recent decades makes it essential to predict water inflow from rivers to lakes (WIRL) trend and unveil the dominant influencing driver, particularly in the context of climate change. The raw time serie...
Artificial intelligence in the era of planetary health: insights on its application for the climate change-mental health nexus in the Philippines.
International review of psychiatry (Abingdon, England)
Jun 22, 2024
This review explores the transformative potential of Artificial Intelligence (AI) in the light of evolving threats to planetary health, particularly the dangers posed by the climate crisis and its emerging mental health impacts, in the context of a c...
A hybrid SWAT-ANN model approach for analysis of climate change impacts on sediment yield in an Eastern Himalayan sub-watershed of Brahmaputra.
Journal of environmental management
Jun 20, 2024
The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The a...
Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014.
Environmental pollution (Barking, Essex : 1987)
Jun 19, 2024
Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the...
Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HEAT Center study protocol.
BMJ open
Jun 18, 2024
INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the compl...
Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.
Environmental science and pollution research international
Jun 15, 2024
Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive netwo...
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