# High-Performance Computing and Artificial Intelligence for Geosciences
## 2 Contributions  
The editors acknowledge all contributions, and we are delighted to introduce a collection of eleven selected high-quality research papers in this book. [PERSON] and colleagues [1] conducted high-performance computing in flood behavior modeling, using the two-dimensional Saint-Venant equations as examples. The equations were discretized using finite difference methods with the explicit leapfrog scheme, considering initial and boundary conditions. They employed MPI, OpenMP, Pthread, and OpenCL to achieve large-scale heterogeneous parallelism and optimized algorithm performancethrough computation/communication overlap, workgroup optimization, and local memory optimization. Ultimately, their work yielded a well-performing, large-scale, non-uniform parallel solution for the two-dimensional Saint-Venant equations. [PERSON] and colleagues [2] studied the ZTEM (\(Z\)-axis Tipper Electromagnetic Method) electromagnetic field detection method. They performed parallel computations on the inversion algorithm for the two-dimensional ZTEM using MPI. Compared to the serial algorithm, the parallel algorithm achieved acceleration ratios ranging from 1.74 to 3.19 when the number of processes ranged from three to six. [PERSON] and colleagues [3] conducted a series of parallel optimizations on the LASG/IAP Climate System Ocean Model (LICOM 2.1 version), a high-resolution ocean model independently developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences. The optimizations addressed load imbalance, communication optimization, and loop optimization. Additionally, they employed hybrid parallelization using MPI and OpenMP, as well as asynchronous parallel I/O. The optimized version of LICOM 2.1 achieved more than two-fold acceleration compared to the original version. In large-scale parallel simulations, the optimized version of LICOM scaled up to 245,760 processor cores and resolved the wall time issue during the time integration process. [PERSON] and coworkers [4] developed site-level gross primary productivity (GPP) using the GeoMAN model, which incorporates spatio-temporal features and incorporates external environmental factors, to predict GPP on the Tibetan Plateau. They evaluated the behavior of four models--random forest (RF), support vector machine (SVM), Deep Belief Network (DBN), and GeoMAN--to predict GPP for nine flux observation sites on the Tibetan Plateau. The GeoMAN model outperformed the other models. These findings are valuable for our understanding of the capability of deep learning models in predicting GPP while aligning with the fundamental knowledge of related fields. [PERSON] and coworkers [5] proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion (MLATC). They first analyzed the reasons for missing landslide displacement data and designed the corresponding missing dataset. Then, based on the characteristics and internal correlation of landslide displacement monitoring data, they introduced the establishment process of the mean-based low-rank tensor completion prediction model. Finally, they used the proposed method to complete and predict the random missing and non-random missing landslide displacement data. The results of the model were consistent with the original monitoring data and showed good performance in completing and predicting landslide displacement, providing valuable insights for processing missing data and predicting landslide displacement. [PERSON] and coworkers [6] proposed a new method called Dual Encoder Transform (DualET) for the short-term prediction of photovoltaic (PV) power. The DualET model contained wavelet transform and sequence decomposition blocks for the extraction of information features from image and sequence data, respectively, to improve the correlation of spatial and temporal features.