Search Results (198)

In this research, surface Au concentration measurements are considered as a spatially correlated geochemical field associated with deep occurrences of disturbing masses using real geological exploration data from the Novo-Khairuzovsky gold deposit in East Kazakhstan. The approach is based on the relationship between potential-field continuation problems and reconstruction of subsurface geological anomalies from surface observations. The considered approaches include Tikhonov and Lavrentiev regularization, SVD, and TSVD. Special attention is given to regularization parameter selection using the L-curve method, Morozov discrepancy principle, and GCV. Comparative computational analysis is performed to evaluate the accuracy, stability, and efficiency of these methods in solving first-kind Fredholm integral equations. Results are assessed using error metrics and spatial visualization of reconstructed fields within a Geographic Information System (ArcGIS), enabling consistent geospatial interpretation. Results show that Lavrentiev regularization with L-curve criterion provides the most stable and reliable reconstruction across all depths, achieving high correlations ($R=0.8876$ at 100 m and $R=0.8049$ at 200 m) with low reconstruction errors. Tikhonov regularization performs acceptably at 100 m but becomes less stable at greater depths. Among spectral methods, TSVD improves stability compared with classical SVD, while standard SVD shows weak correlations and larger reconstruction errors due to high noise sensitivity. Full article

In hot and humid areas, combined shadows from buildings and trees are critical to imagery involving outdoor thermal environments, but are difficult to quantify in complex building–tree intertwined surroundings. This study introduces a shadow geographical ratio (SGR) to quantify combined shadow coverage and proposes a panoramic-photo-based measurement (PPM) framework to compute SGR. Field validation on the campus of Xiamen University, China, using UAV imagery as reference, gave a mean relative error of 1.69% and an RMSE of 0.03 for PPM-derived SGR. The results further showed that SGR was strongly correlated with the sky view factor (SVF) and exhibited significantly higher sensitivity to thermal environment and comfort indicators than SVF, except for relative humidity. Conversely, SVF correlated more strongly with thermal stability. The PPM framework features pedestrian-scale characterization, minimal hardware requirements, and robust error control. The SGR and PPM frameworks offer a practical method for shadow data acquisition in complex environments, supporting microclimate optimization in hot and humid climates. This approach facilitates field data collection and simulation calibration, advancing future studies of shadows. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Accurately predicting the peak height of the F2 layer (hmF₂) is crucial for radio communications, satellite navigation, and space weather studies, yet traditional empirical models often lack precision. To address this, we developed a global hmF₂ prediction model using a Multilayer Perceptron (MLP) neural network, based on COSMIC radio occultation observations from 2007 to 2023. Evaluated on independent test sets from 2014 and 2019, the MLP model achieved correlation coefficients of 0.877 and 0.853 with root mean square errors of 22.3 km and 19.1 km, respectively, significantly outperforming the machine learning approaches like XGBoost and Transformer. The model also demonstrated strong generalization on an independent validation set constructed from 2014 and 2019 GIRO data, with a correlation of 0.785 and RMSE of 26.1 km, surpassing NPHM, and XGBoost. SHAP interpretability analysis identified geographic latitude, cosine of latitude, solar F10.7 index, and annual/daily harmonic terms as the most influential physical features. Error analysis showed a mean prediction error of −3.2 km and a standard deviation of 21.8 km, with stable performance during quiet periods and larger errors primarily during disturbed conditions. This study provides a reliable tool for high-accuracy hmF₂ prediction and enhances the understanding of the physical mechanisms controlling its variability. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

Background/Objectives: To evaluate the challenges and limitations of a deep learning model for automated choroidal boundary detection in eyes with geographic atrophy (GA) using spectral-domain OCT (SD-OCT), and to assess the workflow efficiency of an AI-assisted manual verification approach. Methods: In this retrospective study, total 5723 scans (Heidelberg Spectralis) with GA were analyzed. A previously validated tool (NMI ChoroidAI) was used to segment the choroidal inner (CIB) and outer (COB) boundaries. We compared the “AI-assisted” workflow (automated segmentation followed by manual verification) against “manual segmentation only” in terms of accuracy and time consumption. Slice-wise boundary errors were graded as 0 (accurate), 1 (≤33% deviation), 2 (33–66% deviation), or 3 (>66% deviation). Outcomes included error rates and weighted F₁ score (and precision where applicable). Total time for manual-only segmentation versus AI-assisted verification was recorded. -Interreader variability was assessed between the two readers using intraclass correlation coefficient. Results: For CIB, only 5.2% of B-scans showed any deviation (strictly accurate in 94.8%), with weighted F₁ score 0.97 and precision 1.00. COB was more error-prone: 19.0% of B-scans showed deviation; however, when minor deviations were considered acceptable, COB acceptability increased to 94.2% (i.e., 5.8% remained >33% deviated). Only 13.2% of B-scans required minor manual correction. For a 97-scan volume, processing time decreased from an average of 7 h (manual only) to 45 min (AI + human verification), an approximate 90% reduction in manual effort. Inter-reader agreement was high (ICC 0.923 for CIB and 0.938 for COB). Conclusions: Although the deep learning model exhibits limitations in COB detection due to artifacts, it serves as a valuable assistive tool. Our model substantially reduces human effort, but mandatory human verification is required to correct boundary errors caused by hyper-transmission before use in clinical trials. Full article

This study empirically examines the relationship between digital banking and financial inclusion in Fiji, a small island developing state with geographically dispersed populations and limited access to traditional banking infrastructure. Using annual panel data from eight financial institutions—six commercial banks and two non-bank financial institutions—covering 2012–2024, the analysis accounts for cross-sectional dependence, heteroskedasticity, and serial correlation through Driscoll–Kraay panel-corrected standard errors, while robustness checks using the generalized method of moments (GMM) address potential endogeneity concerns. The results indicate that digital banking is positively associated with higher levels of financial inclusion in Fiji. Both the baseline model, which includes only digital banking, and the extended model, which incorporates banking-sector and macroeconomic controls, show consistent associations. From a policy perspective, the findings provide empirical support for strengthening digital financial infrastructure and regulatory frameworks to promote inclusive finance in small island economies. Overall, the study contributes to the limited empirical literature on digital finance in such contexts and offers insights for policymakers and financial institutions seeking to expand financial inclusion. Full article

Reliable cropland mapping in Karst landscapes is hindered by high topographic heterogeneity and landscape fragmentation. Focusing on the Yunnan–Guizhou Plateau in Southwest China, this study evaluates the accuracy and spatial consistency of seven global land cover products (i.e., GlobeLand30, CLCD, GLC_FCS30, CACD, ESA WorldCover, Esri Land Cover, and FROM-GLC10) against the Third National Land Survey released by China’s Ministry of Natural Resources. Furthermore, we employed Multiscale Geographically Weighted Regression (MGWR) to diagnose key impact factors. The results reveal that the 10 m ESA WorldCover offers superior reliability (OA = 0.81, $R^2$ = 0.84), whereas GLC_FCS30 exhibits the weakest performance among the evaluated datasets (OA = 0.72, $R^2$ = 0.29), highlighting significant uncertainty in this complex terrain. Crucially, MGWR diagnostics (adjusted $R^2=0.923$) uncover how mapping uncertainty is driven by spatially non-stationary environmental constraints. Landscape fragmentation was identified as the primary global driver, exhibiting a consistent negative correlation with accuracy and indicating that the mixed pixel dilemma is the pervasive error source. In contrast, topographic slope operated as a dominant local constraint, with its inhibitory effect intensifying specifically in high-relief gorges where terrain shadowing compromises optical signals. Based on these mechanism diagnostics, we propose a region-adaptive decision framework integrating multi-source fusion and temporal logic to specifically target these topography- and fragmentation-induced uncertainties in future mapping. Full article

Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and availability. To address these limitations, a rice mapping framework suitable for different geographical environments was developed based on a random forest (RF) by combining time-series harmonic analysis (HANTS) with Sentinel-1 and Sentinel-2 multi-source data. To address these limitations, a rice mapping classification algorithm for different geographical environments was developed by combining Harmonic Analysis of Time Series (HANTS) with Sentinel-1/2 multi-source data. The research obtained annual maps of single-season and double-season rice in the research area from 2019 to 2024, with a spatial resolution of 10 m. The results indicated that the Sentinel-1, Sentinel-2, GEE, and HANTS algorithm can effectively support the yearly mapping of single- and double-season paddy rice in Anhui Province, China. The resultant paddy rice map has a high accuracy with overall accuracies exceeding 92% and Kappa coefficients above 0.84. HANTS effectively captures key phenological features of paddy rice, and it can especially enhance the discrimination between single- and double-season rice; compared to existing rice mapping products, the proposed approach reduces classification errors by an average of 3.92% in six major rice-producing cities, each with cultivation areas exceeding 1 million hectares; spatial correlation analysis indicates substantial heterogeneity in rice cultivation patterns across northern, central, and southern Anhui, associated with both biophysical and anthropogenic factors. These results indicate that integrating phenological data with machine learning can enhance the accuracy of long-term, high-resolution crop monitoring, and annual rice maps will offer valuable support for food security assessment, water resource management, and policy planning. Full article

This study presents a hybrid modeling framework aimed at enhancing climate mitigation strategies by evaluating the predictive power of sustainability indicators using both statistical analysis—correlation metrics, regression modeling, distribution tests—and artificial neural networks (ANNs). The analysis centers on variables critical to climate outcomes, including renewable energy use in transport and electricity, greenhouse gas emissions from production, and aggregated target completion values. The findings identify renewable energy usage in transport as the primary predictor of improved performance in the Sustainable Development Report (SDR), followed by overall target completeness, electricity-based renewables, and production-related emissions. Multidimensional interaction analyses highlight a synergetic link between transport renewables and target achievement, underscoring their strategic relevance for climate mitigation efforts. The ANN models demonstrate high predictive accuracy and minimal error, affirming the model’s suitability for scenario-based climate forecasting. Results offer actionable intelligence for policymakers and climate stakeholders to optimize resource allocation and accelerate low-carbon transitions. The study acknowledges limitations, namely, the relatively small dataset and EU-centric analysis, and recommends future extensions to more geographically diverse datasets and the incorporation of advanced econometric techniques and AI frameworks to improve generalizability and predictive potency. Full article

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

Accurate measurement of semantic similarity between geographic terms is a fundamental challenge in geographic information science, directly influencing tasks such as knowledge retrieval, ontology-based reasoning, and semantic search in geographic information systems (GIS). Traditional ontology-based approaches primarily rely on a narrow set of features (e.g., semantic distance or depth), which inadequately capture the multidimensional and context-dependent nature of geographic semantics. To address this limitation, this study proposes an ontology-driven semantic similarity model that integrates a backpropagation (BP) neural network with multiple ontological features—hierarchical depth, node distance, concept density, and relational overlap. The BP network serves as a nonlinear optimization mechanism that adaptively learns the contributions of each feature through cross-validation, balancing interpretability and precision. Experimental evaluations on the Geo-Terminology Relatedness Dataset (GTRD) demonstrate that the proposed model outperforms traditional baselines, including the Thesaurus–Lexical Relatedness Measure (TLRM), Word2Vec, and SBERT (Sentence-BERT), with Spearman correlation improvements of 4.2%, 74.8% and 80.1%, respectively. Additionally, comparisons with Linear Regression and Random Forest models, as well as bootstrap analysis and error analysis, confirm the robustness and generalization of the BP-based approach. These results confirm that coupling structured ontological knowledge with data-driven learning enhances robustness and generalization in semantic similarity computation, providing a unified framework for geographic knowledge reasoning, terminology harmonization, and ontology-based information retrieval. Full article