Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting

no code implementations • 18 Jul 2023 • Zhengjing Ma, Gang Mei

The findings from this study will contribute to understanding landslide behavior in a new way and make the proposed approach applicable to other complex disasters influenced by internal and external factors in the future.

Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction

no code implementations • 24 May 2021 • Zhengjing Ma, Gang Mei, Salvatore Cuomo, Francesco Piccialli

In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations.