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High Performance Computing and Artificial Intelligence for Geosciences

In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Sub...

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Bibliographic Details
Main Author: Wang, Yuzhu
Other Authors: Jiang, Jinrong, Wang, Yangang
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Particle Swarm Optimization
Machine Learning
Licom
2d Forward Modeling
Tipper
Finite Difference Method
K-means Clustering
Meteorological Model
N/a
Satellite Images
Association Rule Mining
Deep Learning
Submarine Landslide
Missing Data
Time Series
Parallel Computing
Gross Primary Productivity
Image Enhancement
Geological News
Bert
Tensor Completion
Disaster Precursor Identification
Autoregressive Norm
Interdisciplinary
Photovoltaic Power Forecasting
Ztem
Information Technology Industries / Bicssc
Attention Mechanism
Spatial Distribution
Early Warning
Geoman Model
Landslide
Convolutional Neural Networks
Hazard Susceptibility
Parallel Optimization
Apriori Algorithm
Heterogeneous Computing
Saint-venant Equations
Transformer
Inversion
Mineral Identification
Semantic Segmentation
Pspnet
Parallel Algorithm
Crf
Displacement Prediction
Gray Relation Analysis
Named Entity Recognition
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Subsequently, Huang et al. proposed a named entity recognition method for geological news based on BERT model. Yang et al. proposed an automatic landslide identification method to solve the problem of the time-consuming nature and low efficiency of traditional landslide identification methods. Du et al. analyzed the potential of unsupervised machine learning methods for submarine landslide prediction. Wang et al. performed parallel computations on the inversion algorithm of the two-dimensional ZTEM. Xu et al. used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides. Furthermore, Cao et al. proposed a new method called dual encoder transform (DualET) for the short-term prediction of photovoltaic power. Hao et al. conducted a series of parallel optimizations and large-scale parallel simulations on the high-resolution ocean model. Wang et al. proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion. Finally, Yang et al. developed a measure of site-level gross primary productivity (GPP) using the GeoMAN model.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (188 p.)
ISBN:9783036581811
9783036581804
books978-3-0365-8181-1

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