Search Results (302)

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

Tunnel seepage in heterogeneous ground can trigger hydrogeological hazards such as concentrated water inflow, groundwater depletion, deformation of surrounding structures, and subsequent eco-environmental degradation. However, these processes are still commonly evaluated using deterministic models that neglect the spatial variability of hydrogeological parameters. To address this limitation, this study develops a stochastic hydro–geo–mechanical–ecological framework that integrates random field theory with physics-informed neural networks (PINNs) for hazard evaluation and rapid prediction of tunnel seepage responses. The spatial variability of key parameters, including permeability and porosity, is characterized using the Karhunen–Loeve expansion and embedded into coupled governing equations for unsaturated–saturated seepage, seepage–stress interaction, and groundwater–soil–vegetation responses. A PINN surrogate model with random-field inputs is then constructed to predict hydraulic head, tunnel inflow, displacement, groundwater depth, vegetation coverage, and soil physicochemical indices, while simultaneously quantifying uncertainty. A karst tunnel case in Chongqing, China, is used to demonstrate the proposed framework. The results show that spatial heterogeneity promotes preferential flow paths and intensifies seepage-induced hazards compared with deterministic mean simulations, leading to larger groundwater drawdown, stronger ecological degradation, and greater overall response variability. The proposed PINN achieves high predictive accuracy (R² > 0.97) and reduces single-case computational time from hours to seconds, enabling efficient multi-scenario evaluation and uncertainty-aware risk assessment. This framework provides a physically consistent and computationally efficient tool for evaluating water-related hazards and long-term environmental impacts in underground engineering. Full article

High-permeability reservoirs at the extra-high water-cut stage commonly exhibit preferential flow, limited sweep expansion, and complex remaining-oil occurrence. To clarify the pore-scale mechanisms controlling waterflood sweep and remaining-oil retention, this study integrates CT-assisted core flooding and microfluidic chip visualization using a high-permeability sandstone core from the Guantao Formation in the Bohai Bay Basin. The CT-assisted core flooding experiment was used to quantify the stage-wise evolution of pores swept by the water phase, while the microfluidic experiment was used to visualize displacement pathways, local bypassing, and remaining-oil morphology under controlled pore-network conditions. The results show that waterflood sweep exhibits clear stage-wise evolution. During the low water-cut stage, injected water preferentially advances through large pore channels, resulting in limited sweep efficiency. With increasing water cut, pores newly swept by the water phase gradually shift from large pores to medium and small pores, accompanied by increasing displacement pressure. Under the present experimental conditions, the lower radius limit of pores newly swept by the water phase is approximately 7.54 μm, corresponding to a capillary force of about 0.9 MPa. When the injected volume exceeds approximately 2.5 PV, the sweep efficiency approaches a plateau and increases only from 0.72 to 0.75 at 5.0 PV, indicating that approximately 25% of the pore space remains difficult to be effectively swept. Image-based classification indicates that remaining oil can be divided into six occurrence types: clustered, porous, columnar, dead-end, film-like, and granular. Clustered and porous are the dominant occurrence types, accounting for a combined 59.7% of the total remaining oil. Pore-structure heterogeneity controls the microscopic sweep boundary through the combined effects of intra-unit structural dispersion and cross-unit structural contrast, which together regulate capillary resistance, seepage resistance, preferential flow, local bypassing, and remaining-oil retention. Microfluidic observations further show that permeability contrast and displacement velocity affect pore-scale displacement pathways and remaining-oil morphology. These findings provide experimental evidence for understanding the lower sweep-radius limit and remaining-oil occurrence mechanisms in high-permeability heterogeneous reservoirs at the extra-high water-cut stage, while the chip-scale velocity effects should be interpreted as pore-scale mechanistic evidence and require further validation before field-scale application. Full article

Freeze-induced damage in concrete is governed by complex interactions between pore-scale phase transition and macroscopic mechanical response, while the underlying pore structure is typically difficult to observe directly. This study proposes an integrated framework for porosity inversion in concrete under freeze–heaving conditions, combining mechanical modeling, finite element simulation, and deep learning. A mechanics-based model is first developed to describe frost-heaving behavior in porous concrete, accounting for elastoplastic deformation of the matrix and partial volumetric expansion induced by pore water freezing. Based on this formulation, a parametric finite element model with randomly distributed pores is constructed to generate datasets linking pore characteristics to full-field deformation responses. Building upon these physics-consistent data, a deep learning framework is established to reconstruct pore distribution directly from three-component strain fields. The model employs a Vision Transformer backbone to capture global deformation patterns and incorporates a Kolmogorov–Arnold Network-based nonlinear mapping to enhance representation of the highly nonlinear inverse relationship. The results demonstrate that the proposed approach achieves accurate pore reconstruction and porosity prediction with stable convergence and satisfactory generalization performance across different porosity levels. The study provides a physically interpretable and computationally efficient pathway for linking deformation fields to internal pore structure, offering new potential for non-destructive characterization and durability assessment of concrete in cold-region environments. Full article

China has established the world’s largest municipal wastewater treatment system through rapid infrastructure expansion over the past two decades. However, under the transition from infrastructure expansion toward urban renewal and low-carbon development, wastewater systems are increasingly challenged by regional imbalances and structural inefficiencies. Existing studies have primarily focused on individual facilities or specific operational issues, while multidimensional system-level assessments remain limited. To address this gap, this study proposed a multidimensional assessment framework for evaluating wastewater system development in China from three dimensions: infrastructure adequacy, operational performance, and adaptive capacity. Based on national and provincial statistical data, regional disparities and development patterns were systematically analyzed using correlation analysis and hierarchical cluster analysis. Results showed that treatment capacity expansion in several provinces outpaced sewer network development, resulting in low hydraulic loading rates and underutilized facilities. Extraneous water infiltration remained a widespread issue, increasing unnecessary wastewater handling, energy consumption, and treatment burden. Reclaimed water development was influenced more strongly by policy-oriented planning and water resource constraints than by economic level alone. In addition, eastern coastal provinces generally demonstrated stronger infrastructure adequacy and operational performance, whereas several western and northeastern provinces remained constrained by insufficient adaptive capacity and sewer coordination. Overall, China’s wastewater sector is transitioning from treatment-oriented expansion toward system-oriented renewal. Future strategies should prioritize sewer rehabilitation, hydraulic efficiency improvement, reclaimed water integration, and adaptive infrastructure planning. The proposed framework can support future infrastructure monitoring, regional policy evaluation, and low-carbon wastewater system transformation. Full article

Cultivated Land “Non-grain” Rectification is reshaping crop allocation across China, yet whether the policy promotes or impedes agricultural growth remains contested. This paper argues that the same uniform regulation generates spatially heterogeneous outcomes along a continuous topographic relief: strict enforcement on contiguous plain farmland raises compliance costs for horticultural production and displaces it toward higher-elevation counties, where land-use rules bind less tightly and micro-climates favor cash crops. Using a panel of 2077 Chinese counties from 2019 to 2023, we construct a municipal-level measure of rectification intensity from government work reports and examine how its effect varies with county-level terrain relief. The results show that the marginal effect of policy intensity on agricultural value added rises monotonically with terrain, turning from negative in flat plains to increasingly positive beyond 0.5–1.0 km of relief; at the sample mean a one-standard-deviation increase in policy intensity raises agricultural value added by about 0.36 percent, and at 2 km of relief by 1.16 percent. The mechanism is spatial reallocation, not land expansion. Rectification shrinks horticultural area in plains and expands it in mountains. A Moran’s I test confirms this: counties with very different terrain show opposite changes in orchard cover. Further heterogeneity tests indicate that rectification primarily promotes the relocation and expansion of fruit orchards toward higher-relief counties. The growth effect is stronger where transport networks are denser, whereas water endowment does not significantly moderate the effect. Results are robust to alternative keyword classifications, concurrent-policy controls, and two instrumental-variable strategies. Full article

Urban design that improves microclimate can significantly enhance the ecological livability of human settlements, while the climate-adaptive wisdom of applying local water-net landscapes to modern urban renewal requires further validation. To investigate the optimization mechanism of waterscape on microclimate comfort, this study focuses on the public space of Xinsheng District in the Suzhou water-net region. By integrating continuous incremental multi-scenario form design, computational fluid dynamics (CFD) multi-physics simulation, and climate sensation evaluation, we reproduce the spatial differentiation of microclimate and comfort gradients across multi-hour periods during hot summer daytime within the built-up environment involving waterbodies, vegetation, and buildings. Consequently, an indicator of comfort improvement efficiency (CIE) is proposed to measure the spatial effectiveness of per-unit-area water surface expansion on climate sensation. Results show that when controlling other morphological parameters and designing three incremental waterbody scenarios—no water surface, 50% water, and 100% waterscape—the relative comfort area expanded across all time periods as water increased. This implies that waterscape variations exert a positive effect on microclimate suitability. However, during the expansion of water area at each time, the CIE was higher in the 0–50% initial stage of water surface increase compared to the 50–100% later morphological stage. Therefore, this study reveals the stepwise nonlinear trend by which increased water area in the built-up environment improves the climate suitability of waterfront spaces. Furthermore, under constraints of equivalent area and other geometric forms, a more dispersed and networked waterscape was found to be a superior spatial strategy. This confirms the microclimate wisdom of the water-net landscape in the Jiangnan locality, providing form optimization guidance for ecologically oriented urban renewal design. Full article

The expansion of paper-based and wearable microwave electronics demands conductors that are highly conductive, finely patterned, mechanically robust, and compatible with low-cost, biodegradable substrates. This study reports a laser-scribing strategy for high-performance copper–liquid metal (Cu–LM) conductors on paper based on laser sintering of Cu–LM composite particles, with an auxiliary adhesive transfer step to facilitate integration on flexible substrates. Laser-induced reactive sintering creates a network wherein sintered liquid metal and CuGa₂ acts as a conductive bridge, interconnecting the dispersed Cu particles. This provides efficient electron transport pathways, achieving a high conductivity of 4.2 × 10⁶ S/m under optimal laser conditions, surpassing that of pure eutectic gallium–indium (EGaIn) alloys. The self-healing nature of LM enables exceptional mechanical flexibility and stable electrical performance under severe deformation. The utility of this platform is demonstrated by a miniaturized microwave liquid level sensor that provides multi-parameter water-level detection and sensor calibration. These results establish laser-scribed Cu–LM on paper as a low-cost and disposable option for high-performance microwave sensors and flexible wireless electronics. Full article

This study investigates how land use changes upstream and the Kamal Khan Dam have reshaped patterns of water allocation and intensified hydropolitical tensions in the Helmand/Hirmand Transboundary River Basin (HTRB). Remote sensing and deep learning techniques were employed to analyze land use changes from 2012 to 2024. After radiometric and atmospheric corrections were applied to Landsat imagery, pixel-based classification was performed using a Deep Belief Network model. Additionally, hydrological changes were analyzed using Sentinel-2 data, with attention paid to the diversion of water flows toward the Godzareh Depression. The classification results revealed considerable expansion of agricultural land downstream of the Kajaki Dam, primarily at the expense of rangeland, forest, and barren land. Moreover, sentinel imagery confirmed that, following the commissioning of the Kamal Khan Dam in 2021, systematically diverted Helmand/Hirmand hydrosystem flows toward the Godzareh Depression. Also, this study applied the painted water framework to analyze the evolution of hydropolitical dynamics in the HTRB across two critical periods, 1954–1980 and 2010–2025. The analysis revealed a fundamental transformation from green water-dominated natural flow regimes to infrastructure-controlled systems characterized by concurrent increases in both yellow water (controlled but not immediately consumed) and red water (controlled and consumed). The Kamal Khan Dam’s operationalization represents a pivotal inflection point, dramatically expanding Afghanistan’s yellow water reserves. This dual expansion of controlled water categories, empirically documented through satellite imagery, confirms the emergence of negative hydrohegemony in the basin. Consequently, Iran’s position has transitioned from a historically stable painted water class to one characterized by critical dependency. Full article

Global water pollution driven by industrial and agricultural expansion has resulted in the widespread occurrence of persistent contaminants, particularly pharmaceuticals and heavy metals, in groundwater systems. Conventional treatment methods often prove inefficient, costly, and environmentally unsustainable, highlighting the need for innovative in situ remediation technologies. Permeable Reactive Barriers (PRBs) have emerged as a promising and energy-efficient solution for the long-term purification of contaminated aquifers. Their efficiency arises from passive operation, relying on natural groundwater flow to promote pollutant removal through adsorption, ion exchange, precipitation, and redox-driven transformations. This review emphasizes the superior performance of materials such as Activated Carbon, Biochar, Zeolites, and Zero-Valent Iron (ZVI) in the immobilization and reduction in pharmaceuticals and metal ions. Key challenges to PRB longevity include permeability loss and reactive media depletion due to mineral precipitation and biofouling. Advances in hybrid PRB configurations, coupled with electrokinetic (EK) and bioreactor systems, and predictive modeling, particularly Artificial Neural Networks (ANNs), offer pathways to enhance performance, optimize design, and ensure sustainable operation. Overall, PRBs represent a scalable and environmentally sound approach to groundwater remediation, with future progress relying on the development of multifunctional, regenerable materials and integrated design strategies. Full article

Cruise ships and superyachts have experienced significant global expansion throughout the 21st century. Although the growth in cruise passenger numbers was temporarily disrupted by the COVID-19 pandemic, occupancy rates have since rebounded and even exceeded pre-pandemic levels. This study highlights the significant environmental impact of cruise ships and luxury yachts, particularly in terms of air emissions and marine pollution. Emission levels associated with different fuel types and marine engines are analysed, including the average emissions generated by the Norwegian Cruise Line fleet while docked in ports, as well as the estimated emission reductions achievable through the implementation of onshore power supply systems. To identify environmentally preferable fuel options, a hybrid ANN/MCDM framework is applied. The weighting coefficients of eight evaluation criteria are determined using the Artificial Neural Network/Extreme Learning Machine (ANN/ELM) model, ensuring an objective and data-driven assessment of their relative importance. The ANN/ELM model was trained using emission and fuel-related data collected from the literature and industry reports, and its performance was validated using standard validation procedures, achieving satisfactory predictive accuracy for determining the weighting coefficients. The final ranking of eight fuel alternatives is subsequently performed using the Ranking Alternatives by Weighting of Evaluated Criteria (RAWEC) method. The considered alternatives include conventional and emerging marine fuels currently used in practice or under technological development (A₁–A₈), while the optimization criteria (C₁–C₈) encompass major air pollutants (CO₂, NOx, SOx, CO, PM, CH₄), the fuel cost-to-consumption ratio, and the potential impact on water pollution. The water pollution criterion is assessed qualitatively using the Saaty scale. The integrated ANN/ELM–RAWEC approach enables a systematic comparison of marine fuels and supports the identification of options with the lowest overall environmental impact. Full article

In order to investigate the effects of bentonite and glass fiber on the macroscopic mechanical properties and microscopic mechanisms of cement soil in dry environments, a series of laboratory tests were conducted in this study, including drying tests under controlled environments (30 °C, 50% humidity), unconfined compressive strength (UCS) tests, digital image processing technology, and scanning electron microscopy (SEM) analyses. The moisture evaporation law, surface crack development process, UCS variation, and microstructure evolution of cement soil with different mix proportions (bentonite content: 0–9%; glass fiber content: 0–0.5%) were systematically analyzed. The results show that bentonite can significantly enhance the water retention capacity of cement soil, reduce the water evaporation rate, and increase the unconfined compressive strength by filling internal pores to densify the microstructure. Glass fibers form a three-dimensional network structure in the matrix, exerting a bridging effect to inhibit crack initiation and propagation, and optimize the mechanical properties. The unconfined compressive strength increases significantly with an increase in bentonite content (3–9%), and the optimal fiber content for strength improvement is determined as 0.3%. The synergistic effect of bentonite and fibers optimizes the interfacial bonding force between fibers and the matrix, which remarkably improves the anti-cracking performance of cement soil. Specifically, when the bentonite content is 6–9% and the fiber content is 0.3–0.5%, the cement soil maintains complete integrity after drying, with no obvious cracks on the surface. SEM analysis reveals that the addition of bentonite and fibers inhibits the expansion and connection of internal voids, avoiding the cycle of “void enlargement–stress concentration–crack propagation”. This study provides a scientific basis for the engineering application of cement soil in a dry environment. Full article

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

With the rapid expansion of metro tunnel construction, safety risks such as collapse, water inrush, and gas explosion have become increasingly critical. Existing warning models often lack fine-grained disaster type identification and dynamic risk assessment capabilities. This paper proposes a two-stage intelligent warning framework based on multi-source data fusion. First, a dual-autoencoder structure (MLP-AE and LSTM-AE) extracts deep features from static geological parameters and dynamic monitoring sequences. Then, a multilayer perceptron (MLP) classifier identifies four typical states: normal, collapse, water/mud inrush, and gas explosion. Subsequently, a regression model predicts a continuous risk score, mapped to three risk levels: Safe, Moderate Risk, and Significant Risk. Experimental results demonstrate that, compared with Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Bayesian Network (BN), the proposed framework achieves superior performance in risk level identification, with an accuracy of 91% and an F1-score of 0.87. Notably, it exhibits particularly strong recall for severe (Level III) risks, which is crucial for practical engineering applications. The proposed framework provides a practical and intelligent approach for safety warning in metro tunnel construction. Full article