Inundation maps with spatial and temporal distribution of the water depths are essential for protecting the population in case of pluvial flood events. Creating these maps in operational forecasting is currently not possible with traditional physical...
During rice cultivation, common rice diseases and pests such as Rice blast, Bacterial blight, Brown-planthopper and Leaf-folder will significantly affect the yield and quality. The current model is limited to detecting rice diseases or pests alone, a...
The global carbon-climate system is a highly complex and dynamic network characterized by multiple feedback loops between interconnected components. Addressing the risks of climate change requires active intervention across these components (Atmosphe...
This study advances a novel multilayer network model to explore the connection between different aspects of Technological Innovation in European Union (EU) countries. We follow a fuzzy clustering approach and consider three variables: Research and De...
Recent developments in the theory of fuzzy graphs have led to many extensions for modeling real-world problems involving uncertainty. Among these, competition graphs are crucial for representing competitive and ecological systems. In this study, the ...
The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutan...
BACKGROUND: Cholera, caused by Vibrio cholerae, is a global health challenge, spreading through water in areas lacking clean water and sanitation. Since 2021, the reemergence of cholera cases has increased significantly in endemic regions in Africa. ...
Vegetation serves as the most critical carbon reservoir within terrestrial ecosystems and plays a vital role in mitigating global climate change. Australia features a vast and diverse landscape, ranging from dense eucalyptus forests to sparse woodlan...
Multi-step forecasting is crucial for capturing future streamflow variations and managing water resources but remains challenging due to limited accuracy of upstream flow forecasts and meteorological predictions over lead times. While data-driven met...
Predicting short-term passenger flow in urban rail transit is crucial for intelligent and real-time management of urban rail systems. This study utilizes deep learning techniques and multi-source big data to develop an enhanced spatial-temporal long ...
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