Earth Science Informatics最新文献

{"title":"A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze-and-excitation network for hyperspectral image classification","authors":"Mehmet Emin Asker, Mustafa Güngör","doi":"10.1007/s12145-024-01469-2","DOIUrl":"https://doi.org/10.1007/s12145-024-01469-2","url":null,"abstract":"<p>Hyperspectral image classification is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose significant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classification, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efficiently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refines these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, reflecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classification accuracy with lower computational costs.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190727","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 framework for microscopic grains segmentation and Classification for Minerals Recognition using hybrid features","authors":"Ghazanfar Latif, Kévin Bouchard, Julien Maitre, Arnaud Back, Léo Paul Bédard","doi":"10.1007/s12145-024-01478-1","DOIUrl":"https://doi.org/10.1007/s12145-024-01478-1","url":null,"abstract":"<p>Mineral grain recognition is an extremely important task in many fields, especially in mineral exploration, when trying to identify locations where precious minerals can possibly be found. The usual manual method would be to collect samples; a specialized individual using expensive equipment would manually identify and then count the grain minerals in the sample. This is a tedious task that is time-consuming and expensive. It is also limited because small portions of areas can be surveyed; even then, it might require extremely long periods. In addition, this process is still prone to human errors. Developing an automatic system to identify, recognize, and count grain minerals in samples from images would allow for more precise results than the time required by humans. In addition, such systems can be fitted on robots that collect samples, take images of the samples, and then proceed with the automated recognition and counting algorithm without human intervention. Vast amounts of land can be surveyed in this way. This paper proposes a modified approach for microscopic grain mineral recognition and classification using hybrid features and ensemble algorithms from images. The enhanced approach also included a modified segmentation approach, which enhanced the results. For 10 classes of microscopic mineral grains, using the modified approach and the ensemble algorithm resulted in an average accuracy of 84.01%. For 8 classes, the average reported accuracy is 94.93% using the Boosting ensemble learning with the C4.5 classifier. The results obtained outperform similar methods reported in the extant literature.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190728","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 novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data","authors":"Shadfar Davoodi, Mohammad Mehrad, David A. Wood, Mohammed Al-Shargabi, Grachik Eremyan, Tamara Shulgina","doi":"10.1007/s12145-024-01474-5","DOIUrl":"https://doi.org/10.1007/s12145-024-01474-5","url":null,"abstract":"<p>Effective drilling planning relies on understanding the rock mechanical properties, typically estimated from petrophysical data. Real-time estimation of these properties, especially static Young's modulus (<span>({E}_{sta})</span>), is crucial for geomechanical modeling, wellbore stability, and cost-effective decision-making. In this study, predictive models of <span>({E}_{sta})</span> were developed using mudlogging data from two vertically drilled wells (A and B) in the same field. <span>({E}_{sta})</span> was estimated from petrophysical data across the studied depth range in both wells using a field-specific equation. Outlier data were identified and removed by evaluating the cross plot of mechanical specific energy and drilling rate for Well A. The data from Well A were then randomly divided into training and testing sets. The algorithms, multi-layer perceptron neural networks, random forests, Gaussian process regression (GPR), and support vector regression, were adjusted and applied to the training data. The resulting models were evaluated on the test data. The GPR model demonstrated the lowest RMSE values in both the training (0.0075 GPa) and testing (0.4577 GPa) phases, indicating superior performance. To further assess the models, the overfitting index and scoring techniques were employed, revealing that the GPR model exhibited the lowest overfitting value and outperformed the other models. Consequently, the GPR model was selected as the best-performing model and was analyzed using Shapley additive explanation to evaluate the influence of each input feature on the output. This analysis indicated that depth had the greatest effect, while rotation speed had the least impact on the model's output. The application of the GPR model to predict <span>({E}_{sta})</span> in Well B demonstrated its high generalization capability. Therefore, it can be confidently stated that with additional data, this model could be effectively applied to similar depth ranges in other wells within the field. The study introduces innovations by applying GPR to predict <span>({E}_{sta})</span> from mudlogging data, addressing outlier impact on predictions, and developing a real-time <span>({E}_{sta})</span> prediction model for drilling.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190729","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":"Determination of the stress concentration factor adjacent an extracted underground coal panel using the CART and MARS algorithms","authors":"Mohammad Rezaei, Hazhar Habibi, Mostafa Asadizadeh","doi":"10.1007/s12145-024-01476-3","DOIUrl":"https://doi.org/10.1007/s12145-024-01476-3","url":null,"abstract":"<p>In this study, classification and regression tree (CART) and multivariate adaptive regression spline (MARS) models are proposed to predict the stress concentration factor (SCF) around an extracted underground coal panel. Models are trained and tested using 120 collected datasets with 100 series allocated for models training and 20 datasets reserved for testing. For SCF prediction using the CART and MARS models, input parameters including overburden thickness (H), specific gravity of rock mass (γ), straight distance from the panel edge (D), and height of disturbed zone over the mined panel (H_d) are utilized, employing principal component analysis (PCA) to remove correlations. A predictive tree graph and 17 if–then rules with quantitative outputs are generated from the CART model, while a predictive equation is derived from the MARS technique for SCF prediction. The achieved values of the coefficient of determination (R²) for CART and MARS models are 0.940 and 0.957, respectively. Furthermore, obtained amounts of normalized root mean square error (NRMSE), variant account for (VAF), and performance index (PI) for CART are 0.043 92.473%, and 1.82, respectively. For the MARS model these values are 0.035, 95.419%, and 1.876,. Additionally, performance evaluations of the models using the Wilcoxon Signed Ranks and Friedman non-parametric tests, along with Taylor diagrams and error analysis demonstrate the reliability and suitability of the proposed models for SCF prediction. However, error and accuracy analyses confirm that MARS model yields more precise outputs, achieving 2.57% greater accuracy and 10.84% lower error than the CART model. Furthermore, the importance analysis demonstrated that both H and H_d have the highest importance on the SCF, while γ has the lowest, with importance values of 33.33% and 11.11%, respectively. Models verification based on the field SCF measurement confirms the models validity, as indicated by the relative errors of 6.83 for the MARS model and 7.05 for the CART model. Finally, a comparative analysis based on a case study data validates the practical application of the proposed models.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190730","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 novel technology for unraveling the spatial risk of Natech disasters based on machine learning and GIS: a case study from the city of Changzhou, China","authors":"Weiyi Ju, Zhixiang Xing","doi":"10.1007/s12145-024-01484-3","DOIUrl":"https://doi.org/10.1007/s12145-024-01484-3","url":null,"abstract":"<p>In recent years, technical accidents caused by natural disasters have caused huge losses. The purpose of this study is to develop a mathematical model to predict and prevent the risk of such accidents. The model applied machine learning to predict the risk of such accidents in the hope of providing risk visualization results for local governments. The expected impact of this research will benefit residents and public welfare organizations. In this study, Random Forest (RF), the K-Nearest Neighbor (KNN), the Back Propagation (BP) neural network, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and the Extreme Gradient Boosting (XGBoost) was applied to predict the risk value. At the same time, this study applied ArcGIS to spatially interpolate the risk prediction values to generate the risk map. The results demonstrated that the RF algorithm achieved the highest classification performance among the five algorithms tested. Specifically, the RF algorithm attained an accuracy of 0.874, an F1-Score of 0.887, and an Area Under the Curve (AUC) of 0.984. The three townships with the highest risk were Xueyan, Daibu, and Shanghuang, with the proportion of risk area accounting for 48.39%, 44.34% and 79.64% respectively. This study provides a reference for the local government, which can take targeted measures to prevent and control. For disaster managers, the risks for those high-risk areas should receive sufficient attention. The government should establish a real-time updated disaster database to monitor the development of the situation. Moreover, the development and acquisition of historical disaster data is worthy of encouragement.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190731","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":"Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region","authors":"Hasan Tonbul","doi":"10.1007/s12145-024-01480-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01480-7","url":null,"abstract":"<p>Forest fires pose a serious risk to ecosystems in the Mediterranean region; thus, 2021 fires in the Mediterranean region of Türkiye emphasize the requirement for accurate and interpretable forest fire susceptibility (FFS) mapping. This study presents an innovative approach to FFS mapping for the Mersin, Antalya, and Mugla provinces, integrating machine learning (ML) models with Explainable Artificial Intelligence (XAI). The methodology employs three state-of-the-art ML models: eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Light Gradient-Boosting Machine (LightGBM). These models generated FFS maps using 14 fire conditioning factors, including meteorological, topographic, environmental, and anthropogenic factors. LightGBM demonstrated outstanding performance, acquiring the highest accuracy (0.897), outperforming GBM (0.881) and XGBoost (0.851). McNemar’s statistical test demonstrated significant differences in the predictive capabilities between XGBoost and both GBM and LightGBM, whereas no significant difference was found between GBM and LightGBM. Information Gain and SHapley Additive exPlanations (SHAP) analyses were applied to enhance model interpretability and validate feature importance. Both methods agreed that the most influential variables in FFS are soil moisture, Palmer Drought Severity Index (PDSI), and Land Surface Temperature (LST). On the other hand, SHAP plots revealed complex, nonlinear relationships between these factors and fire susceptibility. At the same time, a high increase in LST enhances the risk of fires; higher soil moisture values and the PDSI decrease the possibility of fire risk. This research also contributes to the concept of FFS mapping interpretability and operational utility with the application of XAI, which establishes a transparent basis for identifying fire risk drivers in Mediterranean ecosystems.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190732","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":"Study on slope stability of ionic rare earth ore combined with chemical action under environmental application","authors":"YunChuan Deng, HongDong Yu, ShiJie Kang, Jie Yang, YinHua Wan","doi":"10.1007/s12145-024-01461-w","DOIUrl":"https://doi.org/10.1007/s12145-024-01461-w","url":null,"abstract":"<p>To study the stability control scheme of chemical grouting agent for ionic rare earth mine slope. The improved chemical grouting agent comprised lime, sodium silicate, silica micro powder, calcium lignosulfonate and other water solvents. The differences between the enhanced chemical grouting agent and the traditional chemical grouting agent were observed by using indicators such as slope displacement and soil nail tension. The improved chemical grouting agent showed a positive stability effect in both simulation and field experiments. The improved chemical grouting agent is more suitable for the slope stability control scenario of an ionic rare earth mine.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190873","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 surrogate model-based ESM parameter tuning scientific workflow management framework for HPC","authors":"Liang Hu, Xianwei Wu, Xilong Che","doi":"10.1007/s12145-024-01460-x","DOIUrl":"https://doi.org/10.1007/s12145-024-01460-x","url":null,"abstract":"<p>In the present era, scientific computation is gradually becoming a primary research method, with an increasing number of researchers engaging in simulation studies on various high-performance computing platforms. Scientific workflows play a crucial role in organizing these complex research tasks effectively. However, poorly managed scientific workflows can lead to wastage of HPC computational resources and fail to alleviate the operational burden on researchers. The parameter optimization of Earth System Models (ESM) poses specific challenges due to its complexity, exacerbating these issues. To address these challenges, we propose a scientific workflow management framework for surrogate-based ESM parameter optimization. This framework consists of four layers: the resource layer, which gathers current resource information; the service layer, which provides various components to ensure the accurate execution of workflows; the management layer, which monitors the execution status of workflows; and the software environment interaction layer, which serves as the interface for data exchange between users and the framework. We monitored a team engaged in tuning CAM parameters before and after adopting the framework, and the results showed significant improvements in operation numbers, task execution time, and storage resource consumption after deploying the framework. This validates that our proposed scientific workflow management framework effectively addresses the challenges in user operations and resource management during surrogate-based ESM optimization processes, demonstrating the potential of our framework.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190925","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 artificial neural network and least squares regression technique in developing novel models for predicting rock parameters","authors":"C. C. Agoha, A. I. Opara, D. C. Bartholomew, L. J. Osaki, U. K. Agoha, J. O. Njoku, F. B. Akiang, E. T. Epuerie, O. C. Ibe","doi":"10.1007/s12145-024-01464-7","DOIUrl":"https://doi.org/10.1007/s12145-024-01464-7","url":null,"abstract":"<p>This study was carried out within the offshore Niger Delta Basin to generate novel predictive models for estimating rock parameters. MATLAB was employed in obtaining models for four different rock parameter relationships including unconfined compressive strength (UCS) against bulk density, UCS against sonic transit time (STT), shear wave velocity against STT, and permeability against bulk density using multiple ordinary least-squares regression (OLSR) methods. Also, the Adaptive-Neuro Fuzzy Inference System (ANFIS) artificial intelligence network was utilized for modeling and optimization of the data. Statistical tools including the Sum of Squares Total (SST), the Sum of Squares Error (SSE), the Sum of Squares Regression (SSR), and Correlation Coefficient (R-squared) were applied in investigating the prediction performances of the models. Results of OLSR analysis show that only the UCS against bulk density model gave high prediction performance in all the OLSR models with R-squared values of 0.8637, 0.8848, 0.8216, 0.9956, and 0.8108 for linear, quadratic, power, logarithmic, and exponential models respectively. ANN model results revealed that UCS against bulk density, UCS against STT, and shear wave velocity against STT models all gave high prediction performances with respective R-squared values of 0.89635, 0.99365, and 0.52703, while the permeability against bulk density model gave low performance (0.03378). These findings imply that all the OLSR models can be applied for the prediction of rock UCS from bulk density information only, while ANN-generated models can be used in predicting UCS from bulk density and STT, in addition to shear wave velocity from STT in the study area and similar geologic environments.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190772","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":"Analyzing meteorological factors for forecasting PM10 and PM2.5 levels: a comparison between MLR and MLP models","authors":"Nastaran Talepour, Yaser Tahmasebi Birgani, Frank J. Kelly, Neamatollah Jaafarzadeh, Gholamreza Goudarzi","doi":"10.1007/s12145-024-01468-3","DOIUrl":"https://doi.org/10.1007/s12145-024-01468-3","url":null,"abstract":"<p>Over the past twenty years, the Middle East has experienced a surge in air pollution and dust, resulting in a range of issues affecting both people and the environment. Monitoring particulate matter (PM₁₀ and PM_2.5) has long been essential in assessing air quality. Thus, creating precise and proficient predictive models to estimate particulate matter concentrations is imperative for effectively managing and reducing air pollution. The estimation of seasonal and intra-annual PM concentrations was conducted in this study through the use of MLR and MLP models. A diverse range of meteorological parameters, including evaporation, temperature, wind speed, visibility, precipitation, and humidity, were employed along with aerosol optical depth (AOD). During autumn, the MLR and MLP models exhibited impressive performances. For PM10, the R values were 0.7 and 0.79, whereas for PM_2.5, they were 0.7 and 0.81, respectively. The MLP’s superior correlation between the observed and estimated seasonal and intra-annual PM concentrations was noteworthy, as it consistently favored PM2.5 and highlighted the superiority of the ANN-MLP model over MLR. The predictive data underscored a correlation between PM concentration and the four seasons, emphasizing the seasonal impact on PM levels. Sensitivity analysis revealed that relative humidity (RH) was the primary factor influencing the intra-annual levels of both PM₁₀ and PM_2.5. This study offers valuable insights into comprehending the formation process, implementing effective control measures, and establishing predictive models for PM, all aimed at proficiently managing air quality.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190771","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}