Search Results (239)

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes. Full article

Natural gas, a key low-emission energy source with significant strategic value in modern energy systems, necessitates accurate forecasting of its market price to ensure effective policy planning and economic stability. This paper proposes an ensemble framework to enhance natural gas price forecasting accuracy across multiple temporal scales (weekly and monthly) by constructing hybrid models and exploring diverse ensemble strategies, while balancing model complexity and computational efficiency. For weekly data, an Autoregressive Integrated Moving Average (ARIMA) model optimized via 5-fold cross-validation captures linear patterns, while the Long Short-Term Memory (LSTM) network captures nonlinear dependencies in the residual component after seasonal and trend decomposition based on LOESS (STL). For monthly data, the superior-performing model (ARIMA or SARIMA) is integrated with LSTM to address seasonality and trend characteristics. To further improve forecasting performance, three diverse ensemble techniques including stacking, bagging, and weighted averaging are individually implemented to synthesize the predictions of the two baseline models. The bagging ensemble method slightly outperforms other models on both weekly and monthly data, achieving MAPE, MAE, RMSE, and $R^2$ values of 9.60%, 0.3865, 0.5780, and 0.8287 for the weekly data, and 11.43%, 0.5302, 0.6944, and 0.7813 for the monthly data, respectively. The accurate forecasting of natural gas prices is critical for energy market stability and the realization of sustainable development goals. Full article

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

Temporal and spatial variations in water temperature were analyzed from 1998 to 2023 across two contrasting southeast Basque coast estuaries: the deeper, stratified estuary of Bilbao and the shallower, mixed estuary of Urdaibai. We assessed long-term trends, seasonality, intra- and inter-estuary differences, and links to hydro-meteorological drivers using time-series decomposition, clustering, cumulative sum, regression, and correlation analyses. The largest differences in interannual and seasonal patterns occurred between outer neritic and shallow transitional waters. Most water masses warmed overall, with increases until 2003–2006, followed by cooling until 2013–2015, and sharp warming in 2020–2023. The strongest trends (0.24–0.25 °C decade⁻¹) occurred in middle-estuary waters, while inner above-halocline waters of the stratified estuary showed no trend or slight cooling. The strongest warming occurred in spring, particularly in the easternmost mixed estuary (0.49 °C decade⁻¹), especially in May (0.88 °C decade⁻¹). Seasonal minima and maxima occurred earlier in surface transitional waters than in neritic and deep transitional waters of the stratified system. Over time, temperature maxima advanced, minima were delayed, shortening the warming phase, and springs became warmer, extending the warm season. Air temperature was the main driver of water temperature trends, while river flow modulated patterns at annual and seasonal scales, with negative correlations with temperature, mainly in spring. Full article

Accurate prediction of river runoff is significant for flood control, water resource allocation, and basin ecological management. Despite the promise of integrating signal decomposition with deep learning, current decomposition-based hybrid models face critical forward data contamination: decomposition algorithms improperly access future test data in full-series applications, artificially inflating prediction accuracy. In contrast, the stepwise decomposition method currently proposed leads to high computational costs. To address this limitation, we introduce a novel framework integrating segmented decomposition sampling with a multi-input neural network. Specifically, a hybrid forecasting model combining Seasonal-Trend decomposition using Loess (STL) and Convolutional Long Short-Term Memory (CNN-LSTM) networks was implemented for daily runoff estimation. Method reliability was evaluated using historical runoff data from Huaxian Station in China’s Weihe River Basin, with comparative experiments conducted against established single and hybrid models. The results showed that the proposed framework can effectively avoid future information leakage and simultaneously improve prediction accuracy. For 1–3-day-ahead Nash-Sutcliffe efficiency (NSE) at Huaxian Station, the STL-CNN-LSTM model achieved values of 0.96, 0.83, and 0.80, respectively—representing improvements of 5.49%, 5.06%, and 12.68% over the VMD-CNN-LSTM model. This STL-based configuration outperformed the standalone LSTM counterpart by 23.08%, 9.21%, and 17.65% in NSE, respectively. Therefore, the proposed framework, which incorporates the segmented decomposition sampling method and a multi-input neural network, proves to be both practical and reliable. Full article

While humanity stands at a critical point—one future leading toward sustainability, equity, and resilience, the other toward escalating conflicts, ecological collapse, and irreversible loss—climate change emerges as one of the most urgent challenges of the 21st century. The Global South, specifically the northwestern South American region, lacks model confidence and reports on current climatic conditions due to gaps in historical data. This study, therefore, presents temperature and precipitation trends in the highest city on the equator, Quito, Ecuador, from 2004–2024. Six different districts were analyzed for maximum, average, and minimum temperatures, as well as cumulative precipitation, in terms of monthly and annual statistics, using Seasonal-Trend Decomposition. Over the past two decades, this Andean city has warmed by an average of +0.95 °C, with minimum temperatures rising at rates twice the global urban average of extreme urban heat islands (+2.47 °C), while precipitation has nearly doubled in rapidly developing parts of the city. These profound changes, shaped by urban expansion, El Niño–Southern Oscillation variability, and climate change, demand urgent adaptation in water management, urban planning, and climate resilience strategies, as well as comparative studies with rural Ecuador to differentiate local vs. regional climate signatures. Full article

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. Full article

Nowadays, climate change is one of the significant threats humanity faces. Many researchers have documented its effects on water availability and vulnerable systems. This study examines the long-term precipitation record (1965–2019) from the Tulcea station, located just 4 km from the Danube Delta Biosphere Reserve (DDBR), to evaluate the impact of climate change on precipitation variability, which can significantly affect biodiversity in this protected area. We integrated change point detection (CPD), stationarity tests, trend analysis, and series decomposition to characterize shifts and patterns in the time series. The Lee & Heghinian test detected a change point (CP) in all data series, whereas the Hubert segmentation methods and Cumulative Sum Method (CUSUM) found fewer series that present at least a CP. The Mann–Kendall (MK) trend test and Innovative Trend Analysis (ITA) indicated an increasing trend in the annual, monthly, and October precipitation series. The Seasonal-Trend decomposition using Loess STL decomposition found the highest seasonality indices in June and July. The Ensemble Empirical Mode Decomposition (EEMD) emphasizes a substantial difference in the seasonal cycle. The results indicate a high variability in the precipitation pattern, with periods of high precipitation followed by dry periods. Full article

High energy-consuming industrial parks are characterized by high base-load-to-peak-valley ratios, overlapping production cycles, and megawatt-scale step changes, which significantly complicate short-term load forecasting. To tackle these challenges, this study proposes a novel forecasting framework that combines hierarchical multimodal decomposition with a hybrid deep learning architecture. First, Maximal Information Coefficient (MIC) analysis is applied to identify key input features and eliminate redundancy. The load series is then decomposed in two stages: seasonal-trend decomposition uses the Loess (STL) isolates trend and seasonal components, while variational mode decomposition (VMD) further disaggregates the residual into multi-scale modes. This hierarchical approach enhances signal clarity and preserves temporal structure. A parallel neural architecture is subsequently developed, integrating an Informer network to model long-term trends and a bidirectional gated recurrent unit (BiGRU) to capture short-term fluctuations. Case studies based on real-world load data from a typical industrial park in northeastern China demonstrate that the proposed model achieves significantly improved forecasting accuracy and robustness compared to benchmark methods. These results provide strong technical support for fine-grained load prediction and intelligent dispatch in high energy-consuming industrial scenarios. Full article

Straw return is widely adopted to promote agricultural sustainability, but it can also increase cadmium (Cd) bioavailability in contaminated paddy soils, potentially leading to higher Cd accumulation in rice grains. Although numerous studies have investigated straw incorporation, the specific differences between early- and late-season straw return regarding Cd dynamics within double-cropping rice systems remain inadequately characterized. To address this knowledge gap, we conducted a two-year field experiment comparing early-rice (ER) and late-rice (LR) straw return, complemented by controlled pot experiments simulating ER (ER-S, ER-CK; July–September 2023) and LR (LR-S, LR-CK; December 2022–March 2023) straw incorporation. The results revealed that the Total-Cd exhibited an upward trend following both ER and LR straw incorporation. The ER treatment caused a rapid yet short-lived increase in CaCl₂-extractable Cd (CaCl₂-Cd) concentration, peaking around 60 days following straw return and exhibiting a 28.83% increase compared to the LR treatment. In contrast, the LR treatment induced a slower but more prolonged Cd release, with CaCl₂-Cd concentration peaking around 210 days and exhibiting a 34.89% increase relative to the ER treatment. Additionally, at the late-rice stage, grain Cd concentration in the ER treatment increased by 23.64% relative to the LR treatment. In the subsequent year, grain Cd concentrations in the LR treatment increased significantly by 32.12% to 45.08% compared to the ER treatment for both early- and late-rice crops. These differences were attributed to variations in straw decomposition rates, soil pH, and redox potential between warm, aerobic summer–autumn conditions and cooler, anaerobic winter–spring conditions. This suggests that returning late-rice straw constitutes an elevated hazard to soil health and rice safety compared to early-rice straw return. Full article

With the accelerating global energy transition, wind power has become a core pillar of renewable energy systems. However, its inherent intermittency and volatility pose significant challenges to the safe, stable, and economical operation of power grids—making ultra-short-term wind power prediction a critical technical link in optimizing grid scheduling and promoting large-scale wind power integration. Current forecasting techniques are plagued by problems like the inadequate representation of features, the poor separation of features, and the challenging clarity of deep learning models. This study introduces a method for the prediction of wind energy using spatiotemporal contrastive learning, employing seasonal trend decomposition to encapsulate the diverse characteristics of time series. A contrastive learning framework and a feature disentanglement loss function are employed to effectively decouple spatiotemporal features. Data on geographical positions are integrated to simulate spatial correlations, and a convolutional network of spatiotemporal graphs, integrated with a multi-head attention system, is crafted to improve the clarity. The proposed method is validated using operational data from two actual wind farms in Northwestern China. The research indicates that, compared with typical baselines (e.g., STGCN), this method reduces the RMSE by up to 38.47% and the MAE by up to 44.71% for ultra-short-term wind power prediction, markedly enhancing the prediction precision and offering a more efficient way to forecast wind power. Full article

Temperate grasslands are highly sensitive to climate change and play a crucial role in terrestrial carbon cycling. In the context of global warming, heterotrophic respiration (Rh) has intensified, contributing significantly to atmospheric CO₂ emissions. However, seasonal patterns of Rh, particularly differences between the growing season (GS) and non-growing season (non-GS), remain poorly quantified. This study used daily eddy covariance data from multiple flux towers combined with MODIS GPP and NPP products to estimate Rh across temperate grasslands from 2002 to 2021. We examined interannual variations in GS and non-GS Rh contributions and assessed their relationships with key hydrothermal variables. The results showed that mean Rh during GS and non-GS was 527 ± 357 and 341 ± 180 g C m⁻² yr⁻¹, respectively, accounting for 57.8 ± 14.6% and 42.2 ± 14.6% of the annual Rh. Moreover, GS Rh exhibited a declining trend, while non-GS Rh increased over time, indicating a gradual convergence in their seasonal contributions. This pattern was primarily driven by increasing drought stress in GS and warmer, moderately moist conditions in non-GS that favored microbial activity. Our findings underscore the necessity of distinguishing seasonal Rh dynamics when investigating global carbon cycle dynamics. Future earth system models should place greater emphasis on seasonal differences in soil respiration processes by explicitly incorporating the influence of soil moisture on the decomposition rate of soil organic carbon, in order to improve the accuracy of carbon release risk assessments under global change scenarios. Full article

Foodborne diseases represent a significant public health challenge worldwide. This study systematically analyzed the temporal dynamics, key predictors, and seasonal patterns of pathogen-specific foodborne diseases using a dataset of 56,970 cases from Zhejiang Province, China, spanning 2014 to 2023. A comprehensive set of 91 candidate variables was constructed by integrating epidemiological, environmental, socioeconomic, and agricultural data. Lasso regression was employed to identify 41 important predictors. Based on these variables, supervised machine learning models (Random Forest and XGBoost) were trained and evaluated, achieving training set classification accuracies of 86% and 87%, respectively, demonstrating robust performance. Feature importance analysis revealed that patient age, food type, climate policy, and processing methods were the most influential determinants, highlighting the combined impact of host, exposure, and environmental factors on disease risk. The results demonstrated significant shifts in the pathogen spectrum over the past decade, including a steady decline in Vibrio parahaemolyticus, an increase in Salmonella after 2016, and persistent seasonal peaks in Norovirus and Vibrio parahaemolyticus during warmer months. Seasonal ARIMA modeling and time-series decomposition further confirmed the critical role of seasonal and trend components in bacterial incidence. Overall, this study demonstrates the value of integrating machine learning and time-series analysis for pathogen-specific surveillance, risk prediction, and targeted public health interventions. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article