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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 18388

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Special Issue Editors

Dr. Yuzhu Wang

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Guest Editor

School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: high-performance computing; parallel computing; artificial intelligence; distributed machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jinrong Jiang

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Guest Editor

Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
Interests: high performance computing; parallel computing; GPU; artificial intelligence

Prof. Dr. Yangang Wang

E-Mail Website
Guest Editor

Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
Interests: artificial intelligence; high performance computing; parallel computing

Special Issue Information

Dear Colleagues,

The Special Issue is devoted to meeting the increasing demand for research on high-performance computing and artificial intelligence in geosciences. Nowadays, geosciences have become one of the most data-rich fields in terms of quantity and diversity. In order to better process and utilize the ever-increasing datasets, there are great challenges to apply the state-of-the-art computing methods to big data in geoscience. The development of the supercomputer has opened up a new path for geoscience research, and its powerful computing and storage capacity provides an unprecedented efficient platform for massive data processing and numerical simulation. At the same time, artificial intelligence based on deep learning and big data has been widely used in natural language processing, image recognition, etc.

This Special Issue aims to encourage researchers to study advanced methods of high-performance computing and artificial intelligence to solve problems of geosciences, including, but not limited to, the following sub-disciplines: atmospheric science, ocean science, geography, geology, and geophysics. These methods can range from improving or training statistical learning, machine learning, or deep learning algorithms and applying them to analyze text, images, GIS, and other data and using high-performance computing to accelerate computational problems in geosciences.

Topics of interest include, but are not limited to, the following:

High-performance computing of earth system models;
Massively parallel computing;
AI-accelerated numerical weather prediction;
Solving geoscience problems on supercomputers;
Knowledge graph construction;
Data mining in geosciences;
Image recognition based on deep learning;
Natural language processing.

Dr. Yuzhu Wang
Prof. Dr. Jinrong Jiang
Prof. Dr. Yangang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

geosciences
high-performance computing
parallel computing
machine learning
deep learning
data mining
artificial intelligence
knowledge discovery

Published Papers (12 papers)

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Editorial

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4 pages, 169 KiB

Open AccessEditorial

High-Performance Computing and Artificial Intelligence for Geosciences

by Yuzhu Wang, Jinrong Jiang and Yangang Wang

Appl. Sci. 2023, 13(13), 7952; https://doi.org/10.3390/app13137952 - 07 Jul 2023

Cited by 1 | Viewed by 910

Abstract

Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system [...] Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

Research

Jump to: Editorial

19 pages, 6512 KiB

Open AccessArticle

Spatial–Temporal Correlation Considering Environmental Factor Fusion for Estimating Gross Primary Productivity in Tibetan Grasslands

by Qinmeng Yang, Ningming Nie, Yangang Wang, Xiaojing Wu, Weihua Liu, Xiaoli Ren, Zijian Wang, Meng Wan and Rongqiang Cao

Appl. Sci. 2023, 13(10), 6290; https://doi.org/10.3390/app13106290 - 21 May 2023

Cited by 2 | Viewed by 1204

Abstract

Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models’ behavior—Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN—in predicting GPP at nine flux observation sites on the Tibetan Plateau. The GeoMAN model achieved the best results (R² = 0.870, RMSE = 0.788 g Cm⁻² d⁻¹, MAE = 0.440 g Cm⁻² d⁻¹). Distance and vegetation type of the flux sites influenced GPP prediction, with the latter being more significant. The different grassland vegetation types exhibited different sensitivity to environmental factors (Ta, PAR, EVI, NDVI, and LSWI) for GPP prediction. Among them, the site located in the alpine swamp meadow was insensitive to changes in environmental factors; the GPP prediction accuracy of the site located in the alpine meadow steppe decreased significantly with the changes in environmental factors; and the GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes, but to a lesser extent than the former. This study provides a good reference that deep learning model is able to achieve good accuracy in GPP simulation when considers spatial, temporal, and environmental factors, and the judgement made by deep learning model conforms to basic knowledge in the relevant field. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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16 pages, 14845 KiB

Open AccessArticle

Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion

by Chenhui Wang and Yijiu Zhao

Appl. Sci. 2023, 13(8), 5214; https://doi.org/10.3390/app13085214 - 21 Apr 2023

Cited by 3 | Viewed by 909

Abstract

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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19 pages, 1328 KiB

Open AccessArticle

Deep Parallel Optimizations on an LASG/IAP Climate System Ocean Model and Its Large-Scale Parallelization

by Huiqun Hao, Jinrong Jiang, Tianyi Wang, Hailong Liu, Pengfei Lin, Ziyang Zhang and Beifang Niu

Appl. Sci. 2023, 13(4), 2690; https://doi.org/10.3390/app13042690 - 19 Feb 2023

Cited by 1 | Viewed by 1269

Abstract

This paper proposes a series of parallel optimizations on a high-resolution ocean model, the LASG/IAP Climate System Ocean Model (LICOM), which was independently developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. The version of LICOM that we used was LICOM 2.1. In order to improve the parallel performance of LICOM, a series of parallel optimization methods were applied. We optimized the parallelization scheme to tackle the problem of load imbalance. Some communication optimizations were implemented, including data packing, the application of the least communication algorithm, and the replacement of communications with calculations. Furthermore, for the calculation procedures, we implemented some mature optimizations and expanded functions in a loop. Additionally, a hybrid of MPI and OpenMP, as well as an asynchronous parallel IO, was used. In this work, the optimized version of LICOM 2.1 was able to achieve a speedup of more than two times compared with the original code. The parallelization scheme optimization and the communication optimization produced considerable improvement in performance in the large-scale parallelization. Meanwhile, the newly optimized LICOM could scale up to 245,760 processor cores. However, for the original version, there was no speedup when scaled up to over 10,000 processor cores. Additionally, the problem of jumpy wall time during the time integration process was also tackled with this optimization. Finally, we conducted a practical simulation from 1993 to 2007 by using the optimized version of LICOM 2.1. The results showed that the mesoscale vortex was well simulated by the model. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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14 pages, 566 KiB

Open AccessArticle

Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

by Haizhou Cao, Jing Yang, Xuemeng Zhao, Tiechui Yao, Jue Wang, Hui He and Yangang Wang

Appl. Sci. 2023, 13(3), 1908; https://doi.org/10.3390/app13031908 - 01 Feb 2023

Cited by 3 | Viewed by 1366

Abstract

The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-term prediction, which is crucial for reliable power system operation. Existing methods focus on coupling satellite images with ground measurements to extract features using deep neural networks. However, a flexible predictive framework capable of handling these two data structures is still not well developed. The spatial and temporal features are merely concatenated and passed to the following layer of a neural network, which is incapable of utilizing the correlation between them. Therefore, we propose a novel dual-encoder transformer (DualET) for short-term PV power prediction. The dual encoders contain wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Moreover, we propose a cross-domain attention module to learn the correlation between the temporal features and cloud information and modify the attention modules with the spare form and Fourier transform to improve their performance. The experiments on real-world datasets, including PV station data and satellite images, show that our model achieves better results than other models for short-term PV power prediction. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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16 pages, 5844 KiB

Open AccessArticle

Disaster Precursor Identification and Early Warning of the Lishanyuan Landslide Based on Association Rule Mining

by Junwei Xu, Dongxin Bai, Hongsheng He, Jianlan Luo and Guangyin Lu

Appl. Sci. 2022, 12(24), 12836; https://doi.org/10.3390/app122412836 - 14 Dec 2022

Cited by 3 | Viewed by 1197

Abstract

It is the core prerequisite of landslide warning to mine short-term deformation patterns and extract disaster precursors from real-time and multi-source monitoring data. This study used the sliding window method and gray relation analysis to obtain features from multi-source, real-time monitoring data of the Lishanyuan landslide in Hunan Province, China. Then, the k-means algorithm with particle swarm optimization was used for clustering. Finally, the Apriori algorithm is used to mine strong association rules between the high-speed deformation process and rainfall features of this landslide to obtain short-term deformation patterns and precursors of the disaster. The data mining results show that the landslide has a high-speed deformation probability of more than 80% when rainfall occurs within 24 h and the cumulative rainfall is greater than 130.60 mm within 7 days. It is of great significance to extract the short-term deformation pattern of landslides by data mining technology to improve the accuracy and reliability of early warning. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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8 pages, 959 KiB

Open AccessArticle

Parallel Computation for Inversion Algorithm of 2D ZTEM

by Mao Wang, Handong Tan, Yuzhu Wang, Changhong Lin and Miao Peng

Appl. Sci. 2022, 12(24), 12664; https://doi.org/10.3390/app122412664 - 10 Dec 2022

Cited by 1 | Viewed by 841

Abstract

ZTEM refers to the Z-axis tipper electromagnetic method. The ZTEM method is an airborne magnetotelluric sounding method based on the difference in rocks’ resistivity using the native electromagnetic field. The method is effective in exploring large-scale structures when the ground is fluctuant. The paper introduces the inversion algorithm of 2D ZTEM named the conjugate gradient method. This method, which avoids solving the Jacobi matrix, is very effective but not effective enough when the model is divided into a big grid. This study can perform further computation using parallel computation and then receive the processed data. We compare the results of the serial algorithm with the result of the parallel algorithm, which proves that the parallel algorithm is correct. When the number of processes is between three and six, the speedup ratio is between 1.74 and 3.19. It improves the effectiveness of the parallel algorithm of 2D ZTEM. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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17 pages, 3991 KiB

Open AccessArticle

Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models

by Xing Du, Yongfu Sun, Yupeng Song, Zongxiang Xiu and Zhiming Su

Appl. Sci. 2022, 12(20), 10544; https://doi.org/10.3390/app122010544 - 19 Oct 2022

Cited by 6 | Viewed by 1279

Abstract

A submarine landslide is a well-known geohazard that can cause significant damage to offshore engineering facilities. Most standard predicting and mapping methods require expert knowledge, supervision, and fieldwork. In this research, the main objective was to analyze the potential of unsupervised machine learning methods and compare the performance of three different unsupervised machine learning models (k-means, spectral clustering, and hierarchical clustering) in modeling the susceptibility of the submarine landslide. Nine groups of geological factors were selected as the input parameters, which were obtained through field surveys. To estimate submarine landslide susceptibility, all input factors were separated into three or four groups based on data features and environmental variables. Finally, the goodness-of-fit and accuracy of models were validated with both internal metrics (Calinski–Harabasz index, silhouette index, and Davies–Bouldin index) and external metrics (existing landslide distribution, hydrodynamic distribution, and liquefication distribution). The findings of k-means, spectral clustering, and hierarchical clustering performed commendably and accurately in forecasting the submarine landslide susceptibility. Spectral clustering has the greatest congruence with environmental geology parameters. Therefore, the unsupervised machine learning model can be used in submarine-landslide-predicting studies, and the spectral clustering method performed best. Furthermore, machine learning can improve submarine landslide mapping in the future with the development of models and the extension of geological data related to submarine landslides. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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15 pages, 3224 KiB

Open AccessArticle

Automatic Identification of Landslides Based on Deep Learning

by Shuang Yang, Yuzhu Wang, Panzhe Wang, Jingqin Mu, Shoutao Jiao, Xupeng Zhao, Zhenhua Wang, Kaijian Wang and Yueqin Zhu

Appl. Sci. 2022, 12(16), 8153; https://doi.org/10.3390/app12168153 - 15 Aug 2022

Cited by 11 | Viewed by 2329

Abstract

A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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15 pages, 1270 KiB

Open AccessArticle

Chinese Named Entity Recognition of Geological News Based on BERT Model

by Chao Huang, Yuzhu Wang, Yuqing Yu, Yujia Hao, Yuebin Liu and Xiujian Zhao

Appl. Sci. 2022, 12(15), 7708; https://doi.org/10.3390/app12157708 - 31 Jul 2022

Cited by 8 | Viewed by 1708

Abstract

With the ongoing progress of geological survey work and the continuous accumulation of geological data, extracting accurate information from massive geological data has become increasingly difficult. To fully mine and utilize geological data, this study proposes a geological news named entity recognition (GNNER) method based on the bidirectional encoder representations from transformers (BERT) pre-trained language model. This solves the problems of traditional word vectors that are difficult to represent context semantics and the single extraction effect and can also help construct the knowledge graphs of geological news. First, the method uses the BERT pre-training model to embed words in the geological news text, and the dynamically obtained word vector is used as the model’s input. Second, the word vector is sent to a bidirectional long short-term memory model for further training to obtain contextual features. Finally, the corresponding six entity types are extracted using conditional random field sequence decoding. Through experiments on the constructed Chinese geological news dataset, the average F1 score identified by the model is 0.839. The experimental results show that the model can better identify news entities in geological news. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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12 pages, 1663 KiB

Open AccessArticle

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

by Junyu Zhang, Qi Gao, Hailin Luo and Teng Long

Appl. Sci. 2022, 12(14), 7055; https://doi.org/10.3390/app12147055 - 13 Jul 2022

Cited by 7 | Viewed by 2201

Abstract

Mineral identification is an important part of geological research. Traditional mineral identification methods heavily rely on the identification ability of the identifier and external instruments, and therefore require expensive labor expenditures and equipment capabilities. Deep learning-based mineral identification brings a new solution to the problem, which not only saves labor costs, but also reduces identification errors. However, the accuracy of existing recognition efforts is often affected by various factors such as Mohs hardness, color, picture scale, and especially light intensity. To reduce the impact of light intensity on recognition accuracy, we propose an efficient deep learning-based mineral recognition method using the luminance equalization algorithm. In this paper, we first propose a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and use this algorithm to process the luminance of the identified samples, and finally use the YOLOv5 model to identify the samples. The experimental results show that our method achieves 95.6% accuracy for the identification of 50 common minerals, achieving a luminance equalization-based deep learning mineral identification method. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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17 pages, 25984 KiB

Open AccessArticle

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations

by Yongmeng Qi, Qiang Li, Zhigang Zhao, Jiahua Zhang, Lingyun Gao, Wu Yuan, Zhonghua Lu, Ningming Nie, Xiaomin Shang and Shunan Tao

Appl. Sci. 2022, 12(11), 5671; https://doi.org/10.3390/app12115671 - 02 Jun 2022

Cited by 3 | Viewed by 1491

Abstract

Large-scale floods are one of the major events that impact the national economy and people’s livelihood every year during the flood season. Predicting the factors of flood evolution is a worldwide problem. We use the two-dimensional Saint-Venant equations as an example and for high-performance computing in modelling the flood behavior. Discretization of the two-dimensional Saint-Venant equations with initial and boundary conditions with the finite difference method in the explicit leapfrog scheme is carried out. Afterwards, we employed a large-scale heterogeneous parallel solution on the “SunRising-1” supercomputer system using MPI, OpenMP, Pthread, and OpenCL runtime libraries. On this basis, we applied communication/calculation overlapping and the local memory acceleration to optimize the performance. Finally, various performance tests of the parallel scheme are carried out from different perspectives. We have found this method is efficient and recommend this approach be used in solving systems of partial differential equations similar to the Saint-Venant equations. Full article

(This article belongs to the Special Issue High Performance Computing and Artificial Intelligence for Geosciences)

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