AIMC Topic: Rain

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Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one Sep 15, 2022
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...
Water Movements Floods Forecasting Deep Learning Rain
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A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze.

Sensors (Basel, Switzerland) Jun 22, 2022
This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration met...
Deep Learning Snow Rain Awareness Disasters
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Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale.

The Science of the total environment Mar 16, 2022
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by m...
Artificial Intelligence Hydrology Cities Cluster Analysis Rain Water Movements
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Investigation of intra - event variations of total, dissolved and truly dissolved metal concentrations in highway runoff and a gross pollutant trap - bioretention stormwater treatment train.

Water research Mar 11, 2022
Metals in stormwater can be toxic to organisms, particularly when occurring in truly dissolved form (fraction <3 kDa). Here, using 153 samples collected during six rains, we investigated intra-events variations of total, dissolved and truly dissolved...
Environmental Pollutants Water Purification Cadmium Water Supply Rain Lead Water Pollutants, Chemical
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Occurrence and concentration of 20-100 μm sized microplastic in highway runoff and its removal in a gross pollutant trap - Bioretention and sand filter stormwater treatment train.

The Science of the total environment Oct 21, 2021
Microplastic pollution of stormwater can be a serious threat to the environment. Gross pollutant trap (GPT) - bioretention treatment trains have been shown previously to treat (inter alia) particulate stormwater pollutants including microplastic part...
Microplastics Water Supply Plastics Rain Environmental Pollutants Water Purification
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Exploring the potential of utilizing unsupervised machine learning for urban drainage sensor placement under future rainfall uncertainty.

Journal of environmental management Jul 9, 2021
Recently, advanced informatics and sensing techniques show promise of enabling a new generation of smart stormwater systems, where real-time sensors are deployed to detect flooding hotspots. Existing stormwater design criteria assume that historical ...
Cities Uncertainty Rain Unsupervised Machine Learning Floods
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Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data.

PloS one Mar 11, 2020
Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Recently, convolutional LSTM has been shown to be successful in solving various complex spatiotemporal ...
Neural Networks, Computer Satellite Imagery Algorithms Rain
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Autonomously Moving Pine-Cone Robots: Using Pine Cones as Natural Hygromorphic Actuators and as Components of Mechanisms.

Artificial life Feb 6, 2020
We have developed autonomously moving pine-cone robots, which are made of multiple joined pine-cone scales for outdoor natural environments. We achieved these natural robots by using pine cones as both natural hygromorphic actuators and components of...
Droughts Rain Pinus Robotics Plant Components, Aerial
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A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model.

PloS one Jan 21, 2020
To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper....
Neural Networks, Computer Airports Rain Models, Theoretical Water Movements Beijing
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A new backpropagation neural network classification model for prediction of incidence of malaria.

Frontiers in bioscience (Landmark edition) Jan 1, 2020
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classifie...
Rain Humidity Geography Humans Neural Networks, Computer Models, Theoretical Incidence Malaria Algorithms Seasons Support Vector Machine India Temperature Reproducibility of Results
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