# High-Performance Computing and Artificial Intelligence for Geosciences
## 2 Contributions
In addition, they proposed a cross-domain attention module to learn the correlation between temporal features and cloud information, and then modified the attention module using alternate forms and Fourier transforms to improve its performance. The model was evaluated on real-world datasets consisting of PV plant data and satellite images, and it outperformed other models in the prediction of short-term PV power generation. [PERSON] and coworkers [7] used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides in Lishan County, Hunan Province, China. They applied the K-means algorithm with particle swarm optimization for clustering and the Apriori algorithm to mine strong correlation rules between the high-speed deformation process of the landslide and rainfall features. This approach enabled them to identify short-term deformation patterns and precursors of disasters. They indicated that the probability of high-speed deformation of this landslide exceeded 80% when the rainfall occurred within 24 h and the accumulated rainfall in 7 days was greater than 130.60 mm. By using data mining technology to extract short-term deformation patterns of landslides, the accuracy and reliability of early warning systems can be improved. [PERSON] and coworkers [8] analyzed the potential of unsupervised machine learning methods for submarine landslide prediction and compared the performance of three different unsupervised machine learning models (K-means, spectral clustering, and hierarchical clustering) in modeling landslide susceptibility. They selected nine sets of geological factors as input parameters, which were extracted through field investigations. To estimate the susceptibility of submarine landslides, all input factors were grouped into three to four clusters based on data characteristics and environmental variables. The performance of the models was evaluated using internal indicators (the Calinski-Harabasz index, silhouette index, and Davies-Bouldin index) and external indicators (existing landslide distribution, hydrodynamic distribution, and liquefaction distribution) to verify model fit and accuracy. The results showed that all three models (K-means, spectral clustering, and hierarchical clustering) performed well in accurately predicting submarine landslides. Spectral clustering was found to be particularly effective in capturing the environmental geological parameters. [PERSON] and coworkers [9] proposed an automatic landslide identification method. Their approach combined deep learning with landslide extraction from remote sensing images, using a semantic segmentation model to automate the landslide recognition process. They evaluated the model's performance using metrics from the semantic segmentation task and tested three popular semantic segmentation models (U-Net, DeepLabv3+, and PSPNet) with different backbone networks. The best recognition accuracy of PSPNet was 91.18% mIoU with the classification network ResNet50 as the backbone network, which proved the deep learning method is feasible and effective for use in landslide recognition. [PERSON] and coworkers [10] proposed a named entity recognition (GNNER) method for geological news based on a bi-directional encoder representation of a converter (BERT) pre-trained language model. The approach addressed drawbacks of traditional word vectors, including the fact that they do not effectively represent the contextual semantics and single extraction effects occur. This approach can also aid the construction of knowledge graphs of geological news. The method involves embedding words in geological news text using a BERT pre-training model, with the resulting word vectors being dynamically obtained and used as input for the model. Next, the word vectors are fed into a bidirectional long- and short-term memory model for further training to obtain contextual features. Finally, the model uses conditional random field sequence decoding to extract six entity types.