Nanjing University of Information Science & Technology

National Climate Center

Nanjing University of Information Sci. & Tech.

Guofu Wang

Guojie Wang

Yanjun Wang

Tong Jiang

Buda Su

Aiqing Feng

Mingyue Lu

Zhen Dong

Zifan Liang

Xikun Wei

Zicong Luo

A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.

High-temperature disaster forecast method based on directed graph neural network

The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into the trained meteorological data fusion model to obtain high-resolution fused meteorological data.

Meteorological big data fusion method based on deep learning

A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model. Combined with the image fusion model based on Laplacian pyramid, the method can provide effective decision-making basis for prevention and mitigation of landslide disasters.

Landslide recognition method based on laplacian pyramid remote sensing image fusion

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Beijing, China

Guofu Wang