Search Results (951)

Slope stability prediction is a critical task in geotechnical engineering, but machine learning (ML) models require large datasets, which are often costly and time-consuming to obtain. This study proposes a domain-driven teacher–student framework to overcome data limitations for predicting the dry factor of safety (FS dry). The teacher model, XGBoost, was trained on the original dataset to capture nonlinear relationships among key site-specific features (unit weight, cohesion, friction angle) and assign pseudo-labels to synthetic samples generated via domain-driven simulations. Six student models, random forest (RF), decision tree (DT), shallow artificial neural network (SNN), linear regression (LR), support vector regression (SVR), and K-nearest neighbors (KNN), were trained on the augmented dataset to approximate the teacher’s predictions. Models were evaluated using a train–test split and five-fold cross-validation. RF achieved the highest predictive accuracy, with an R² of up to 0.9663 and low error metrics (MAE = 0.0233, RMSE = 0.0531), outperforming other student models. Integrating domain knowledge and synthetic data improved prediction reliability despite limited experimental datasets. The framework provides a robust and interpretable tool for slope stability assessment, supporting infrastructure safety in regions with sparse geotechnical data. Future work will expand the dataset with additional field and laboratory tests to further improve model performance. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

This study proposes a feature extraction scheme that fuses accelerometric (ACC) and electromyographic (EMG) data to improve shoulder movement identification in individuals with transhumeral amputation, in whom the clinical need for intuitive control strategies enabling reliable activation of full-arm prostheses is underinvestigated. A novel spatiotemporal warping feature extraction architecture was employed to realize EMG and ACC information fusion at the feature level. EMG and ACC data were collected from six participants with intact limbs and four participants with transhumeral amputation using an NI USB-6009 device at 1000 Hz to support the proposed feature extraction scheme. For each participant, a leave-one-trial-out (LOTO) training and testing approach was used for developing pattern recognition models for both the intact-limb (IL) and amputee (AMP) groups. The analysis revealed that the introduction of ACC information has a positive impact when using windows of length (WLs) lower than 150 ms. A linear discriminant analysis (LDA) classifier was able to exceed the accuracy of 90% in each WL condition and for each group. Similar results were observed for an extreme learning machine (ELM), whereas k-nearest neighbors (kNN) and an autonomous learning multi-model classifier showed a mean accuracy of less than 87% for both IL and AMP groups at different WLs, guaranteeing applicability over a large set of shallow pattern-recognition models that can be used in real scenarios. The present work lays the groundwork for future studies involving real-time validation of the proposed methodology on a larger population, acknowledging the current limitation of offline analysis. Full article

As the main load-bearing components of engineering structures, regular health assessment of reinforced concrete (RC) columns is crucial for improving the service life and overall performance of the structures. This study focuses on the health detection problem of in-service RC columns. By combining deep learning algorithms and acoustic emission (AE) technology, the AE sources of in-service RC columns are located, and the optimal sensor layout form for the health monitoring of in-service RC columns is determined. The results show that the data cleaning method based on the k-means clustering algorithm and the voting selection concept can significantly improve the data quality. By comparing the localization performance of the Back Propagation (BP), Radial Basis Function (RBF) and Support Vector Regression (SVR) models, it is found that compared with the RBF and SVR models, the MAE of the BP model is reduced by 7.513 mm and 6.326 mm, the RMSE is reduced by 9.225 mm and 8.781 mm, and the R² is increased by 0.059 and 0.056, respectively. The BP model has achieved good results in AE source localization of in-service RC columns. By comparing different sensor layout schemes, it is found that the linear arrangement scheme is more effective for the damage location of shallow concrete matrix, while the hybrid linear-volumetric arrangement scheme is better for the damage location of deep concrete matrix. The hybrid linear-volumetric arrangement scheme can simultaneously detect damage signals from both shallow and deep concrete matrix, which has certain application value for the health monitoring of in-service RC columns. Full article

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. However, current methods for detecting soil salinization primarily rely on various environmental covariates, which assess the extent of soil salinization by analyzing the relationship between environmental factors and the accumulation of soil salts. Nonetheless, these conventional environmental covariates often suffer from response delays, making it challenging to promptly reflect the dynamic changes in soil salinity. Solar-induced chlorophyll fluorescence (SIF) has been widely used to assess vegetation photosynthetic efficiency and is considered a direct indicator of plant photosynthetic activity. In contrast, SIF provides a timely means of monitoring the status of plant photosynthesis, indirectly reflecting the impact of soil salinization on plant growth. However, the spatial resolution of SIF products derived from satellites is typically low, which significantly limits the accurate estimation of soil salinization in Xinjiang. This study proposes a novel method for monitoring soil salinization, based on SIF data. The approach employs a Stacking ensemble learning model to downscale SIF data, thereby improving the spatial resolution of soil salinity monitoring. Using the GOSIF dataset, combined with environmental covariates, such as MODIS, the Stacking framework facilitates the fine-scale downscaling of SIF data, generating high-resolution SIF products, ranging from 0.05° to 0.005°, with a spatial resolution of 30 m. This refined SIF data is then used to predict soil electrical conductivity (EC). The experimental results demonstrate that: (1) the proposed Stacking-based SIF downscaling method is highly effective, with a high degree of fit to reference SIF data (R² > 0.85); (2) the high-resolution SIF data, after downscaling, more accurately reflects the spatial heterogeneity of soil salinization, especially in shallow soils (r < −0.6); and (3) models combining SIF and environmental covariates exhibit superior accuracy compared to models that rely solely on SIF or traditional environmental covariates (R² > 0.65). This research provides new data support and methodological advancements for precision agriculture and ecological environmental monitoring. Full article

Concrete infrastructure inspection is essential for maintaining the safety and longevity of urban environments. Traditional manual crack detection methods are labor-intensive, inconsistent, and difficult to scale. Recent advancements in deep learning and computer vision offer automated alternatives, particularly when deployed via unmanned aerial vehicles (UAVs) for enhanced coverage and flexibility. However, achieving real-time performance on embedded systems requires models that are not only accurate but also lightweight and computationally efficient. This study presents CrackDetect-Lite, a comparative analysis of three deep learning architectures for binary crack detection using the SDNET2018 benchmark dataset: CNNSimple (a custom lightweight model), RSNet (a shallow residual network), and MobileVNet (a fine-tuned MobileNetV2). Class imbalance was addressed using a weighted cross-entropy loss function, and models were evaluated across multiple criteria including classification accuracy, crack-class F1-score, inference latency, and model size. Among the models, MobileVNet achieved the best balance between detection performance and deployability, with an accuracy of 90.5% and a crack F1-score of 0.73, while maintaining a low computational footprint suitable for UAV-based deployment. These findings demonstrate that carefully selected lightweight CNN architectures can deliver reliable, real-time crack detection, supporting scalable and autonomous infrastructure monitoring in smart city systems. Full article

Accurate runoff simulation is essential for water resources planning and development projects. At present, the commonly employed runoff simulation approaches are categorized into two types: process- and data-driven models. Process-driven models pertain to the enhancement of the structural framework in conceptual rainfall–runoff models using hydrological principles to estimate runoff but have low accuracy at the monthly scale. Unlike the process-driven models, data-driven models (DDMs) can simulate the relationship between input factors and output runoff data without regard to complex and unknown runoff production and acquire satisfactory simulation results. Here, we comparatively investigate the applicability of DDMs, including traditional shallow DDMs, deep learning-based (DL) models for monthly runoff simulation, and select the Autoregressive (AR) model as the baseline model for comparison. Moreover, four evaluation indicators, including mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R²), are employed to evaluate the runoff simulation effects of the above methods. We systematically compare the AR model, and shallow and deep learning-based DDMs for runoff simulation at four hydrological stations in the Yalong River basin (YRB), respectively. The finding results reveal that the DDMs demand limited data and can offer satisfactory prediction effects. Also, the DL models outperform other shallow DDMs and the AR model in terms of the above evaluation criteria. Full article

Remote sensing imagery (RSI) object detection is critical to many applications, yet mainstream detectors analyse only spatial features and, because of spectral bias, fail to learn high-frequency information adequately, resulting in performance bottlenecks under cluttered backgrounds, distractors, and multi-scale targets, especially small ones. To break these limitations, we propose MSRS-DETR, an end-to-end framework that deeply fuses spatial and frequency cues. The approach introduces three key innovations: (1) C2f_FATNET, a frequency-attention-enhanced lightweight residual backbone that provides richer dual-domain features with fewer parameters; (2) an Entanglement Transformer Block (ETB) in the encoder that refines deep semantics via cross-domain frequency–spatial interaction and suppresses background interference; and (3) S2-CCFF, a shallow-feature-extended bidirectional fusion path that markedly improves the retention and utilisation of fine details for small objects. Experiments on HRSC2016 and ShipRSImageNet demonstrate the effectiveness and generalisation of this spatial–frequency paradigm: relative to the baseline, MSRS-DETR reduces parameters by $29.1\%$, boosts inference speed by $12.4\%$ and $8.4\%$, and raises mAP_50-95 by $1.69\%$ and $2.16\%$, respectively. Full article

Nearshore bathymetry is critical for coastal management and ecology. While airborne hyperspectral remote sensing provides high-resolution image data, obtaining rapid and accurate bathymetric inversion in coastal areas lacking in situ data remains challenging. The widely used Hyperspectral Optimization Process Exemplar (HOPE) achieves high accuracy but suffers from computational inefficiency, making it impractical for large-scale, high-resolution datasets. By contrast, HOPE-Pure Water (HOPE-PW) offers computational efficiency but exhibits limitations in capturing fine-scale spatial patterns of bottom reflectance (ρ), and its applicability in transitional waters between Case I and II types requires further validation. Against this background, we employed machine learning-based substrate classification (support vector machine, random forest, maximum likelihood) in Wenchang coastal waters, China, to constrain ρ estimation in HOPE-PW, with validation using ICESat-2 data that extends its conventional application scenarios. Results demonstrate that when constrained by the optimal classifier (random forest), HOPE-PW achieves comparable accuracy to HOPE in shallow water while reducing runtime by 56% and memory usage by 68%. However, HOPE-PW exhibits slight underestimation in deeper areas, likely because simplification reduces sensitivity to water optical properties. Future research will focus on this issue. This study proposes an efficient and reliable framework for monitoring and evaluating water depth in areas lacking in situ data, offering a practical solution for integrated coastal zone management. Full article

Coalbed methane (CBM) resources hold broad development prospects in China, with deep CBM reservoirs increasingly becoming a focal point for exploration. However, compared to shallow CBM, the factors influencing the productivity of deep CBM are more complex and less studied. This study integrates statistical methods—grey correlation analysis and principal component analysis—with the machine learning approach of random forests, and further employs a fuzzy mathematics-based comprehensive evaluation method to propose a systematic evaluation framework for identifying key controlling factors of productivity. Using field data from the No. 8+9 coal seam in the eastern margin of the Ordos Basin, the results indicate that the primary geological factors affecting cumulative gas production are gas content and coal seam thickness, while the key engineering factors are proppant intensity and proppant volume. These findings align with practical field experience and provide a rational basis for the design of fracturing strategies in deep CBM reservoirs. Full article

In recent decades, technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. The manual delineation and classification of anomalies are time-consuming, and different methods for (semi-)automatic image segmentation have been proposed, based on explicitly formulated rulesets or deep convolutional neural networks (DCNNs). So far, these have not been used widely in archaeological geophysics because of the complexity of the segmentation task (due to the low contrast between archaeological structures and background and the low predictability of the targets). Techniques based on shallow machine learning (e.g., random forests, RFs) have been explored very little in archaeological geophysics, although they are less case-specific than most rule-based methods, do not require large training sets as is the case for DCNNs, and can easily handle 3D data. In this paper, we show their potential for geophysical data analysis. For the classification on the pixel level, we use ilastik, an open-source segmentation tool developed in medical imaging. Algorithms for object classification, manual reclassification, post-processing, vectorisation, and georeferencing were brought together in a Jupyter Notebook, available on GitHub (version 7.3.2). To assess the accuracy of the RF classification applied to geophysical datasets, we compare it with manual interpretation. A quantitative evaluation using the mean intersection over union metric results in scores of ~60%, which only slightly increases after the manual correction of the RF classification results. Remarkably, a similar score results from the comparison between independent manual interpretations. This observation illustrates that quantitative metrics are not a panacea for evaluating machine-generated geophysical data interpretation in archaeology, which is characterised by a significant degree of uncertainty. It also raises the question of how the semantic segmentation of geophysical data (whether carried out manually or with the aid of machine learning) can best be evaluated. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

Background: Tumor-educated platelets (TEPs) represent a promising biosource for non-invasive multi-cancer early detection (MCED). While machine learning (ML) has been applied to TEP data, the integration of explainability to reveal gene-level contributions and regulatory associations remains underutilized. This study aims to develop an interpretable ML framework for cancer detection using platelet RNA-sequencing data, combining predictive performance with biological insight. Methods: This study analyzed 2018 TEP RNA samples from 18 tumor types using seven machine learning classifiers. SHAP (Shapley Additive Explanations) was applied for model interpretability, including global feature ranking, local explanation, and gene-level dependence patterns. A weighted SHAP consensus was built by combining model-specific contributions scaled by Area Under the Receiver Operating Characteristic Curve (AUC). Regulatory insights were supported through network analysis using GeneMANIA. Results: Neural models, including shallow Neural Network (NN) and Deep Neural Network (DNN) achieved the best performance (AUC ~0.93), with Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) also performing well. Early-stage cancers were predicted with high accuracy. SHAP analysis revealed consistent top features (e.g., SLC38A2, DHCR7, IFITM3), while dependence plots uncovered conditional gene interactions involving USF3 (KIAA2018), ARL2, and DSTN. Multi-hop pathway tracing identified NFYC as a shared transcriptional hub across multiple modulators. Conclusions: The integration of interpretable ML with platelet RNA data revealed robust biomarkers and context-dependent regulatory patterns relevant to early cancer detection. The proposed framework supports the potential of TEPs as a non-invasive, information-rich medium for early cancer screening. Full article

The karst terrain of Guangxi, China, characterized by steep slopes and thin residual soils, is highly vulnerable to rainfall-induced shallow landslides. Timely and accurate displacement forecasting is critical for early warning and risk mitigation. However, most existing systems depend on centralized computation, leading to latency and reduced responsiveness. Moreover, conventional forecasting models are often too computationally intensive for edge devices with limited processing resources. To address these constraints, we present EoML-SlideNet, a lightweight forecasting framework designed for resource-limited hardware. It decomposes displacement and triggers into trend and periodic components, then applies the Dual-Band Lasso-Enhanced Latent Variable (DBLE–LV) module to select compact, interpretable features via cross-correlation, LASSO, and VIF screening. A small autoregressive model predicts the trend, while a lightweight neural network captures periodic fluctuations. Their outputs are combined to estimate displacement. All models were evaluated on a single CPU-only workstation to ensure fair comparison. This study introduces floating-point operations (FLOPs), alongside runtime, as practical evaluation metrics for landslide displacement prediction models. A site-specific multi-sensor dataset was developed to monitor rainfall-triggered landslide behavior in the karst terrain of Guangxi. The experimental results show that EoML-SlideNet achieves 2–4 times lower MAE/RMSE than the most accurate deep learning and the lightest baseline models, while offering 3–30 times faster inference. These results demonstrate that low-complexity models can match or surpass the accuracy of deep networks while achieving latency and FLOP levels suitable for edge deployment without dependence on remote servers. Full article

This paper presents a physics-guided machine learning (PGML) approach to model the I–V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with $tanh$ as the basis function is combined with a hypernetwork that dynamically generates its weight parameters. The influence of transconductance is added to the loss function. This model can synchronously predict the output and transfer characteristics of the device. Under the condition of small samples, the prediction error is controlled within 5%, and the $R^2$ value reaches above 0.99. The proposed PGML approach outperforms conventional approaches, ensuring physically meaningful and robust predictions for device optimization and circuit-level simulations. Full article