Earth Science Informatics最新文献

{"title":"Assessment of 30 gridded precipitation datasets over different climates on a country scale","authors":"Alireza Araghi, Jan F. Adamowski","doi":"10.1007/s12145-023-01215-0","DOIUrl":"https://doi.org/10.1007/s12145-023-01215-0","url":null,"abstract":"<p>In many regions of the globe, lack of precipitation data is one of the main factors limiting the undertaking of a wide range of environmental studies. Recent studies have shown that gridded precipitation data were dependable replacements for measured precipitation data. In the current study — the most comprehensive to date over the study area and neighboring regions — 30 gridded precipitation datasets from across Iran were evaluated. To evaluate the accuracy of several available gridded precipitation datasets, measured precipitation data were collected from 40 synoptic weather stations across the country from 2001 to 2013. Various performance metrics such as normalized root mean square error (NRMSE) and Nash–Sutcliffe efficiency (NSE), in addition to the Wilcoxon test, served to evaluate the accuracy of gridded precipitation datasets. The Global Precipitation Climatology Center (GPCC) dataset showed the best accuracy with an overall NRMSE of ~ 37% and a NSE of ~ 0.82, while the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) dataset had the weakest performance with an overall NRMSE of ~ 179% and a NSE of -3.25. Due to the temporal limitations of some gridded datasets, even top-ranked ones, and considering the performance metrics of all evaluated datasets, GPCC, TerraClimate, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) datasets are preferable sources for monthly precipitation over the study area. More studies are needed to expand the results of the current research over the study area and surrounding zones.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"2 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-sections compilation-adjustment method based on 2D-3D linkage in regional three dimensional geological modeling","authors":"Xuechao Wu, Gang Liu, Wenyao Fan, Shijie Peng, Genshen Chen, Junjie Cheng, Yongjin Wu","doi":"10.1007/s12145-024-01221-w","DOIUrl":"https://doi.org/10.1007/s12145-024-01221-w","url":null,"abstract":"<p>In 3D geological modeling methods based on section, cross-section data is an important data source for constructing 3D geological modeling in complex areas. The efficiency and quality of cross-section data compilation-adjustment will directly affect the time and quality of subsequent regional 3D geological model construction. However, current compilation-adjustment methods for cross-section are inefficient and difficult to effectively control their quality. Therefore, in this paper, a cross-section compilation-adjustment method based on 2D-3D linkage is proposed. It deeply integrates the virtual borehole geological structure (VBGS), 2D-3D linkage strategy, and structural geological feature information into the inspection-adjustment process of geologic bodies (faults, strata, rock masses, and ore bodies) structural abnormality (GSA) and elevation consistency at intersections. A complete cross-section compilation and quality control process can be realized by regional structural stratigraphic framework construction, GSA high efficiency analysis and adjustment, elevation errors quick positioning and adjustment at intersections. This new method solves the problem of inconsistency between the complex geological structures and the real structural geological characteristics and overcomes the defects of local fitting abnormality (depressions or sharp protrusions) of geological interfaces caused by elevation errors at intersections. Software tool is developed based on QuantyView 2D/3D GIS platform, and 73 cross-section data of a 1:50,000 modeling area in western Guizhou, China, is taken as an example for practical application verification. Results show that the proposed new method significantly improves the inspection-adjustment efficiency of GSA and elevation errors at intersections. What’s more, quality of cross-sections compilation-adjustment has been effectively controlled, which provides reliable data sources for constructing high quality regional 3D geological models.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"11 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifractal Scaling characteristics of lineament networks of a fractured sandstone aquifer system","authors":"Safia Abdi, Mohamed Chettih","doi":"10.1007/s12145-024-01222-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01222-9","url":null,"abstract":"<p>Underground flow in fractured aquifers depends on the connectivity of the fractures, considered as a set of conduits and the main drainage axes. Quantitative analysis of fracture networks using multifractal characterisation therefore provides the backbone for assessing the connectivity of these networks. The aim of our work is to carry out a multifractal characterisation of lineament networks while performing related geometric, spectral and fractal analyses. The approach used is based on the box counting method to estimate the multifractal spectrum using the method of moments. The generalized fractal dimensions were estimated using the partition function and the multifractal spectrum using the Legendre transform. The work was carried out using a calculation code that we developed ourselves and that we named: 2D Calculation Code for Multifractal Analysis of Fracture Networks (2D-MAFN).Four lineament maps at different scales were analyzed, corresponding to the Upper Jurassic and Cretaceous geological formations of the El Gada region in the Central Algerian Saharan Atlas. The geometric analysis of the lineament networks showed good consistency between the lineament networks and the geological structure of the Atlas Range and its fault network. It also showed that the distribution of the lengths of the lineaments fits the power law. This analysis also revealed that, on a larger scale, diffuse fracturing appears to be more prevalent. In addition, the spectral analysis, through the decrease in spectral power according to a power law, characterizes a self-similar behavior and already seems to prove the scale invariance of the lineaments. The fractal dimension values obtained reflect the extent of fracturing and the degree of complexity of the network of lineaments. These values show that the lineaments are also well correlated with each other. The partition functions show that the points line up on the adjustment lines according to a law characteristic of multifractal behavior. In addition, the curves of generalized dimensions as a function of moments show a clear decrease, highlighting the multifractal nature of the fracturing process. In addition, the multifractal spectra in the form of bell curves also confirm the multifractal process for the four lineament networks analyzed. The results obtained are very encouraging and open up the prospects of modelling fracture networks for a variety of purposes, including assessing the connectivity of a fracture network.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"178 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms","authors":"Mahdieh Jannatkhah, Rouhollah Davarpanah, Bahman Fakouri, Ozgur Kisi","doi":"10.1007/s12145-024-01220-x","DOIUrl":"https://doi.org/10.1007/s12145-024-01220-x","url":null,"abstract":"<p>Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station; Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\u0000","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"21 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139471259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran","authors":"Maryam Jahanbani, Mohammad H. Vahidnia, Hossein Aghamohammadi, Zahra Azizi","doi":"10.1007/s12145-023-01213-2","DOIUrl":"https://doi.org/10.1007/s12145-023-01213-2","url":null,"abstract":"<p>Floods, as natural disasters, impose significant human and financial burdens, necessitating stringent mitigation measures. The recurrent annual incidence of floods precipitates considerable economic setbacks and tragic human casualties. In the realm of disaster management, flood susceptibility mapping has evolved into an indispensable instrument for preemptive intervention. In recent years, the amalgamation of machine learning (ML) methodologies and geographic information systems (GIS) has demonstrated remarkable promise in the realm of flood susceptibility mapping. Nonetheless, the inherent limitations of standalone ML models have constrained their predictive efficacy. Several shortcomings are evident in prior research. These include the failure to utilize contemporary ensemble approaches capable of enhancing performance and the limited exploration of diverse classifier combinations, which are instrumental in augmenting reliability. Simultaneously, there is an absence of current and up-to-date flood susceptibility maps on recent floods within the study area. Hence, this study endeavors to enhance the precision of flood susceptibility mapping, within the Haraz-Neka River basin across Mazandaran province, by harnessing an ensemble of ML models. The research methodology encompassed several pivotal phases. Initially, data about 240 flood sites were meticulously compiled. Subsequently, 70% of this dataset was allocated for training and cartographic elucidations, whereas the remaining 30%, selected at random, served to validate the resultant maps. The analytical framework incorporated a spectrum of influential parameters, encompassing Elevation, Slope, Aspect, Rainfall, land use, Vegetation Differentiation Index (NDVI), Soil Hydrology Groups, Proximity to the River, Distance from Landslides, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Sediment Transport Index (STI) for spatial modeling. The results undeniably highlight the superior performance of the ensemble model compared to its individual counterparts. Validation exercises, leveraging historical flood data, prominently endorsed the AdaBoost algorithm integrated with the Decision Tree classifier as the most efficacious. Garnering an Area Under ROC curve surpassing 0.96, accompanied by an accuracy of 0.93%, a sensitivity of 0.95%, and a specificity of 0.92%, this amalgamation substantiates its prowess. The proposed framework stands poised to empower decision-makers in identifying vulnerable regions and devising efficacious flood risk mitigation strategies.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"65 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Swin transformer for Land Use/ Land Cover change detection using LISS-III Satellite data","authors":"Sam Navin Mohanrajan, Agilandeeswari Loganathan, P. Manoharan, Farhan A. Alenizi","doi":"10.1007/s12145-023-01208-z","DOIUrl":"https://doi.org/10.1007/s12145-023-01208-z","url":null,"abstract":"","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"23 5","pages":"1-20"},"PeriodicalIF":2.8,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139437571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models","authors":"Navin Tony Thalakkottukara, Jobin Thomas, Melanie K. Watkins, Benjamin C. Holland, Thomas Oommen, Himanshu Grover","doi":"10.1007/s12145-023-01218-x","DOIUrl":"https://doi.org/10.1007/s12145-023-01218-x","url":null,"abstract":"<p>Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"90 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam","authors":"Huu Duy Nguyen, Van Hong Nguyen, Quan Vu Viet Du, Cong Tuan Nguyen, Dinh Kha Dang, Quang Hai Truong, Ngo Bao Toan Dang, Quang Tuan Tran, Quoc-Huy Nguyen, Quang-Thanh Bui","doi":"10.1007/s12145-023-01209-y","DOIUrl":"https://doi.org/10.1007/s12145-023-01209-y","url":null,"abstract":"<p>Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of this study was the development of a new method based on remote sensing, deep neural networks (DNNs), and the optimization algorithms Adam, Flower Pollination Algorithm (FPA), Artificial Ecosystem-based Optimization (AEO), Pathfinder Algorithm (PFA), African Vultures Optimization Algorithm (AVOA), and Whale Optimization Algorithm (WOA) to predict groundwater potential in the North Central region of Vietnam. 95 springs or wells with 13 conditioning factors were used as input data to the machine learning model to find the statistical relationships between the presence and nonpresence of groundwater and the conditioning factors. Statistical indices, namely root mean square error (RMSE), area under curve (AUC), accuracy, kappa (K) and coefficient of determination (R²), were used to validate the models. The results indicated that all the proposed models were effective in predicting groundwater potential, with AUC values of more than 0.95. Among the proposed models, the DNN-AVOA model was more effective than the other models, with an AUC value of 0.97 and an RMSE of 0.22. This was followed by DNN-PFA (AUC=0.97, RMSE=0.22), DNN-FPA (AUC=0.97, RMSE=0.24), DNN-AEO (AUC=0.96, RMSE=0.25), DNN-Adam (AUC=0.97, RMSE=0.28), and DNN-WOA (AUC=0.95, RMSE=0.3). In addition, according to the groundwater potential map, about 25–30% of the region was in the high and very high potential groundwater zone; 5–10% was in the moderate zone, and 60–70% was low or very low. The results of this study can be used in the management of water resources in general and the location of appropriate wells in particular.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"62 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep neural network modeling of river discharge in a tropical humid watershed","authors":"Benjamin Nnamdi Ekwueme","doi":"10.1007/s12145-023-01219-w","DOIUrl":"https://doi.org/10.1007/s12145-023-01219-w","url":null,"abstract":"<p>Precise forecast of river discharge is crucial for a variety of sectors, from human activities to the control of environmental hazards, considering growing need for water resources and the effects of climate change. Despite the development of various discharge forecasting models, real-time projections are still difficult. This has necessitated the application of Artificial Intelligence to predict river discharge using satellite data since there is paucity of gauged records in most developing countries. In this research, a 38-year data, obtained from the National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), was used to model the discharge of five selected rivers from South Eastern Nigeria watershed. Deep Neural Networks (DNN) modeling technique was engaged. Back propagation learning algorithms of various network topologies were developed for predicting the river’s discharge with respect to other hydrological properties. The developed model was trained and validated with the raw dataset. Results indicated that relative humidity, atmospheric pressure, wind speed, rainfall intensity, radiation, air temperature, and soil temperature govern the discharge of river. The DNN model accurately predicted the river discharge with the 7–25-25–25-1 network structure, as evidenced by 99.91, 99.62, and 99.01% R for the training, validation, and test. The results of this analysis showed that DNN approach is effective at forecasting river discharge with respect to the hydrological characteristics. Decision-makers in the water and environmental sectors can utilize this knowledge in making an informed sustainable development plan.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"18 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A compound approach for ten-day runoff prediction by coupling wavelet denoising, attention mechanism, and LSTM based on GPU parallel acceleration technology","authors":"Yi-yang Wang, Wen-chuan Wang, Dong-mei Xu, Yan-wei Zhao, Hong-fei Zang","doi":"10.1007/s12145-023-01212-3","DOIUrl":"https://doi.org/10.1007/s12145-023-01212-3","url":null,"abstract":"<p>Deep learning models have a high application value in runoff forecasting, but their prediction mechanism is difficult to interpret and their computational cost is high when dealing with complex hydrological relationships, limiting their feasibility in hydrological process mechanism analysis. To address these concerns, the paper first introduces an attention mechanism (AM) for building a long short-term memory network (LSTM) model with AM in the hidden layer (AM-LSTM). The AM-LSTM model employs attention layers to improve information extraction from hidden layers, resulting in a more accurate representation of the relationships between runoff-related elements. Furthermore, in the hidden layers of the AM-LSTM model, interpretable spatiotemporal attention units are established, which not only improves the model's prediction accuracy but also provides interpretability to the forecasting process. Furthermore, parallelization techniques are used in the paper to address the issue of model runtime cost. Simultaneously, to address the accuracy degradation caused by parallelization, the paper employs wavelet denoising (WD) techniques and builds the WD-AM-LSTM model. This accomplishment enables the runoff forecasting model to predict runoff in real time and with high accuracy. Based on validation using ten-day runoff data from the Huanren Reservoir in the Hun River's middle reaches, the results show that, with two layers and an eight-batch size, the AM-LSTM model outperforms the LSTM model in capturing spatiotemporal runoff features. During the model testing phase, the AM-LSTM model improves the MAE, RMSE, and NSE performance metrics by 8.46%, 13%, and 3.82%, respectively. The WD-AM-LSTM model effectively mitigates the noise impact caused by data parallelization under the conditions of two layers and a batch size of 512, achieving the same level of prediction performance while reducing computational cost by 92.01%. By incorporating attention mechanisms and wavelet denoising techniques, this study obtains high-speed and accurate predictions with interpretable results. It expands the deep learning models' applicability in ten-day runoff forecasting work.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"98 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}