Earth Science Informatics最新文献

{"title":"DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module","authors":"Chonghua Tang, Gang Hu","doi":"10.1007/s12145-024-01479-0","DOIUrl":"https://doi.org/10.1007/s12145-024-01479-0","url":null,"abstract":"<p>The technology for classifying and recognizing underwater targets is crucial for supporting underwater acoustic information countermeasures. The research focus is on the extraction and classification of features of underwater targets. Researchers have conducted an in-depth study from various perspectives. Due to the influence of ambient noise and various operating conditions of different targets, the signal-to-noise ratio of underwater acoustic signals is generally meager. Additionally, the components of these signals are complex, often requiring specific signal pre-processing techniques such as signal enhancement and decomposition. In current methods, there is a primary focus on extracting and classifying features of underwater acoustic signals after multi-step preprocessing. However, these methods do not effectively integrate feature extraction and classification. To address these limitations, we propose a new model called Depthwise Separable Convolutional Attention (DSCA) and use multiple instances of DSCA to construct a neural network, which we call DSCANet. The DSCANet integrates feature extraction and target classification for underwater acoustic targets. The ’target’ in our work should be mentioned as it refers to underwater sources of sound. The structure of DSCANet is unified and simple, and no specific pre-processing of the underwater acoustic signal is necessary. The DSCANet is trained and validated on ShipsEars, an open dataset. It achieves a classification accuracy of 93%, which is the highest in the contrast experiment.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"60 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224818","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":"Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm","authors":"Junjie Jiang, Qizhi Wang, Shihao Luan, Minghui Gao, Huijie Liang, Jun Zheng, Wei Yuan, Xiaolei Ji","doi":"10.1007/s12145-024-01470-9","DOIUrl":"https://doi.org/10.1007/s12145-024-01470-9","url":null,"abstract":"<p>The Taihang Mountains in China span numerous cities, where landslide disasters occur frequently in the mountainous areas, jeopardizing the lives and properties of residents. Consequently, it is of great significance to focus on prevention and control of landslide disasters in the region. Currently, a single model is commonly employed to analyze landslide susceptibility mapping (LSM), but the accuracy of the results fails to meet the demands of early warning, prevention, and control. This paper focuses on the Taihang Mountain area as the research area, organizes the collection of landslide disaster potential points and related influence factor data, and employs the information quantity method to derive a composite machine learning model by coupling with Random Forest (RF) and Extreme Gradient Boosting (XGB), subsequently utilizing the Genetic Optimization Algorithm (GA) to optimize the model. The performance of the composite model is enhanced using the Genetic Algorithm (GA), employing accuracy, regression rate, precision, F1 score, AUC value, and Taylor diagram to evaluate the comprehensive accuracy of the model results, with a susceptibility map generated for comparative analysis. The results demonstrate that the IV-GA-RF model performs optimally (accuracy = 0.956, precision = 0.96, recall = 0.953, F1 score = 0.957, AUC = 0.946 for the testing set, AUC = 0.929 for the training set), with all-around improvement in performance metrics compared to the unoptimized composite model, with metric values improving by 0.044, 0.051, 0.046, 0.044, 0.021 and 0.020, respectively. The IV-GA-RF model exhibits a significant advantage over the IV-GA-XGB algorithm, also optimized using the GA algorithm. The accuracy of the susceptibility map produced by the IV-GA-RF model is superior, as assessed by the Seed Cell Area Index (SCAI) method. The four factors of slope, rainfall, seismicity, and stratigraphic lithology are crucial in determining the occurrence of landslides in the study area. In summary, the IV-GA-RF model can be utilized as an effective model for analyzing landslide disasters, providing a reference for research in this field and contributing scientific insights to disaster prevention and control efforts in the study area; simultaneously, the concept of the composite optimization model introduces new perspectives into this field.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"32 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190777","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":"Using wavelet transform to analyze the dynamics of climatic variables; to assess the status of available water resources in Iran (1961–2020)","authors":"Ali Rezaee, Abolfazl Mosaedi, Aliasghar Beheshti, Azar Zarrin","doi":"10.1007/s12145-024-01433-0","DOIUrl":"https://doi.org/10.1007/s12145-024-01433-0","url":null,"abstract":"<p>In recent years, the effects and consequences of climate change have shown themselves by creating irregularities and trends in the essential climatic variables. In most cases, the trend of climatic variables is associated with periodicity. In this study, the trends and periodicity of these data (precipitation, temperature, evapotranspiration, and net available water (NWA) have been investigated in a period of 60 years in Iran. The Mann–Kendall trend test and Sen’s slope estimator are applied for analyzing the trend and its magnitude. Wavelet transform is used to detect the periodicity of time series and to determine the correlation between NWA and temperature, precipitation, and evapotranspiration in common periodicity. The results show that the stations located in eastern and western Iran have more significant increasing/decreasing trends. Evapotranspiration shows the highest increasing trend in most stations, followed by temperature, while NWA and precipitation have trends at lower significance levels and decreasing direction. The examination of periodicity in time series showed that, among all the studied stations, evapotranspiration has the most extended periodicity with an average length of 8.3 years, followed by NWA, temperature, and precipitation with 7.3 years, 5.8 years, and 5.5 years. The results of the correlations investigation showed that in about 80% of the stations, there is a high correlation between precipitation and NWA in the short-term periodicity and at the end of the studied period. The evapotranspiration variable in most stations has a high correlation in different periodicities with the amount of NWA.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"24 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190778","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":"MSCANet: A multi-scale context-aware network for remote sensing object detection","authors":"Huaping Zhou, Weidong Liu, Kelei Sun, Jin Wu, Tao Wu","doi":"10.1007/s12145-024-01447-8","DOIUrl":"https://doi.org/10.1007/s12145-024-01447-8","url":null,"abstract":"<p>With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"74 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190776","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":"Estimating soil organic carbon using sentinel-2 data under zero tillage agriculture: a machine learning approach","authors":"Lawrence Mango, Nuthammachot Narissara, Som-ard Jaturong","doi":"10.1007/s12145-024-01427-y","DOIUrl":"https://doi.org/10.1007/s12145-024-01427-y","url":null,"abstract":"<p>Soil organic carbon (SOC) is the main component of soil organic matter (SOM) and constitutes the crucial component of the soil. It supports key soil functions, stabilizes soil structure, aid in plant-nutrient retention and release, and promote water infiltration and storage. Predicting SOC using Sentinel-2 data integrated with machine learning algorithms under zero tillage practice is inadequately documented for developing countries like Zimbabwe. The purpose of this study is to evaluate the performance of support vector machine (SVM), artificial neural network (ANN), and partial least square regression (PLSR) algorithms from Sentinel-2 data for SOC estimation. The SVM, ANN and PLSR models were used with a cross-validation to estimate the SOC content based on 50 georeferenced calibration samples under a zero-tillage practice. The ANN model outperformed the other two models by delivering a coefficient of determination (R²) of between 55 and 60% of SOC variability and RMSE varied between 5.01 and 8.78%, whereas for the SVM, R² varied between 0.53 and 0.57 and RMSE varied between 6.25 and 11.39%. The least estimates of SOC provided by the PLSR algorithm were, R² = 0.44–0.49 and RMSE = 7.59–12.42% for the top 15 cm depth. Results with and R², root mean square error (RMSE) and mean absolute error (MAE) for SVM, ANN and PLSR, show that the ANN model is highly capable for capturing SOC variability. Although the ANN algorithm provides more accurate SOC estimates than the SVM algorithm, the difference in accuracy is not significant. Results revealed a satisfactory agreement between the SOC content and zero tillage practice (R², coefficient of variation (CV), MAE, and RMSE using SVM, ANN and PLSR for the validation dataset using four predictor variables. The calibration results of SOC indicated that the mean SOC was 15.83% and the validation mean SOC was 17.02%. The SOC validation dataset (34.17%) had higher degree of variation around its mean as compared to the calibration dataset (29.86%). The SOC prediction results can be used as an important tool for informed decisions about soil health and productivity by the farmers, land managers and policy makers.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"117 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190780","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 method for landslide identification and detection in high-precision aerial imagery: progressive CBAM-U-net model","authors":"Hanjie Lin, Li Li, Yue Qiang, Xinlong Xu, Siyu Liang, Tao Chen, Wenjun Yang, Yi Zhang","doi":"10.1007/s12145-024-01465-6","DOIUrl":"https://doi.org/10.1007/s12145-024-01465-6","url":null,"abstract":"<p>Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"15 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190779","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":"Prediction of soil classification in a metro line from seismic wave velocities using soft computing techniques","authors":"Hosein Chatrayi, Farnusch Hajizadeh, Behzad Shakouri","doi":"10.1007/s12145-024-01435-y","DOIUrl":"https://doi.org/10.1007/s12145-024-01435-y","url":null,"abstract":"<p>At a particular location on the ground, geotechnical measurements of soil properties are utilized to offer information for infrastructure design. Design uncertainty and dependability may increase when little point data is used. Geophysical techniques offer constant geographic information about the soil and are less time-consuming and intrusive. Geophysical data, however, is not expressed in terms of technical specifications. To enable the use of geophysical data in geotechnical designs, correlations between geotechnical and geophysical characteristics are required. The S- and P- seismic wave velocities are the main focus of the present geophysical technique research. Artificial neural network (ANN) models are developed using published data to predict seismic wave velocity and soil classification for seismic site effect evaluation. The results of ANN models using publicly available data demonstrate that seismic wave velocity has a moderate to high degree of accuracy in predicting soil classification. Regression is not as effective as artificial neural networks (ANN) in terms of overall performance. To confirm this, enclosed areas were evaluated to accurately predict soil classification and assess the performance of both ANN and regression models. The artificial neural network predicted the enclosed areas with much higher accuracy.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"9 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190819","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 SVC, k-NN, and LDA machine learning algorithms for improved prediction of Bioturbation: Example from the Subei Basin, China","authors":"Jonathan Atuquaye Quaye, Kwame Sarkodie, Zaixing Jiang, Chenlin Hu, Joshua Agbanu, Stephen Adjei, Baiqiang Li","doi":"10.1007/s12145-024-01450-z","DOIUrl":"https://doi.org/10.1007/s12145-024-01450-z","url":null,"abstract":"<p>Three supervised machine learning (ML) classification algorithms: Support Vector Classifier (SVC), K- Nearest Neighbour (K-NN), and Linear Discriminant Analysis (LDA) classification algorithms are combined with seventy-six (76) data points of nine (9) core sample datasets retrieved from five (5) selected wells in oilfields of the Subei Basin to delineate bioturbation. Application of feature selection via p-score and f-scoring reduced the number of relevant features to 7 out of the 12 considered. Each classifier underwent model training and testing allocating 80% of the data for training and the remaining 20% for testing. Under the model training, optimization of hyperparameters of the SVC (C, Gamma and Kernel) and K-NN (K value) was performed via the grid search to understand the best form of the decision boundaries that provides optimal accuracy of prediction of Bioturbation. Results aided the selection of optimized SVC hyperparameters such as a linear kernel, C-1000 and Gamma parameter—0.10 that provided a training accuracy of 96.17%. The optimized KNN classifier was obtained based on the K = 5 nearest neighbour to obtain a training accuracy of 73.28%. The training accuracy of the LDA classifier was 67.36% which made it the worst-performing classifier in this work. Further cross-validation based on a fivefold stratification was performed on each classifier to ascertain model generalization and stability for the prediction of unseen test data. Results of the test performance of each classifier indicated that the SVC was the best predictor of the bioturbation index at 92.86% accuracy, followed by the K-NN model at 90.48%, and then the LDA classifier which gave the lowest test accuracy at 76.2%. The results of this work indicate that bioturbation can be predicted via ML methods which is a more efficient and effective means of rock characterization compared to conventional methods used in the oil and gas industry.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"24 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190821","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 debris flow susceptibility mapping study considering sample heterogeneity","authors":"Ruiyuan Gao, Di Wu, Hailiang Liu, Xiaoyang Liu","doi":"10.1007/s12145-024-01453-w","DOIUrl":"https://doi.org/10.1007/s12145-024-01453-w","url":null,"abstract":"<p>Susceptibility mapping has been an effective approach to manage the threat of debris flows. However, the sample heterogeneity problem has rarely been considered in previous studies. This paper is to explore the effect of sample heterogeneity on susceptibility mapping and propose corresponding solutions. Two unsupervised clustering approaches including K-means clustering and fuzzy C-means clustering were introduced to divide the study area into several homogeneous regions, each region was processed independently to solve the sample heterogeneity problem. The information gain ratio method was used to evaluate the predictive ability of the conditioning factors in the total dataset before clustering and the homogeneous datasets after clustering. Then the total dataset and the homogeneous datasets were involved in the random forest modeling. The receiver operating characteristic curves and related statistical results were employed to evaluate the model performance. The results showed that there was a significant sample heterogeneity problem for the study area, and the fuzzy C-means algorithm can play an important role in solving this problem. By dividing the study area into several homogeneous regions to process independently, conditioning factors with better predictive ability, models with better performance and debris flow susceptibility maps with higher quality could be obtained.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"10 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190820","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":"EAS $$^2$$ KAM: enhanced adaptive source-selection kernel with attention mechanism for hyperspectral image classification","authors":"Ahmed R. El-gabri, Hussein A. Aly, Mohamed A. Elshafey, Tarek S. Ghoniemy","doi":"10.1007/s12145-024-01466-5","DOIUrl":"https://doi.org/10.1007/s12145-024-01466-5","url":null,"abstract":"<p>Hyperspectral Images (HSIs) possess extensive applications in remote sensing, especially material discrimination and earth observation monitoring. However, constraints in spatial resolution increase sensitivity to spectral noise, limiting the ability to adjust Receptive Fields (RFs). Convolutional Neural Networks (CNNs) with fixed RFs are a common choice for HSI classification tasks. However, their potential in leveraging the appropriate RF remains under-exploited, thus affecting feature discriminative capabilities. This study introduces an Enhanced Adaptive Source-Selection Kernel with Attention Mechanism (EAS<span>(^2)</span>KAM) for HSI Classification. The model incorporates a Three Dimensional Enhanced Function Mixture (3D-EFM) with a distinct RF for local low-rank contextual exploitation. Furthermore, it incorporates diverse global RF branches enriched with spectral attention and an additional spectral-spatial mixing branch to adjust RFs, enhancing multiscale feature discrimination. The 3D-EFM is integrated with a 3D Residual Network (3D ResNet) that includes a Channel-Pixel Attention Module (CPAM) in each segment, improving spectral-spatial feature utilization. Comprehensive experiments on four benchmark datasets show marked advancements, including a maximum rise of 0.67% in Overall Accuracy (OA), 0.87% in Average Accuracy (AA), and 1.33% in the Kappa Coefficient (<span>(kappa )</span>), outperforming the top two HSI classifiers from a list of eleven state-of-the-art deep learning models. A detailed ablation study evaluates model complexity and runtime, confirming the superior performance of the proposed model.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"32 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190685","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}