Search Results (528)

Flood susceptibility mapping in the Yellow River Basin remains challenging due to uncertainties in sample selection and model generalization. This study develops a novel two-step coupling framework that integrates an information value (IV) model with machine learning (ML) to improve reliability. The IV model first identifies stable low-susceptibility zones to select robust non-flood samples, which are then combined with historical flood inventories to train ML models. The SHAP method is applied to quantify factor contributions and interpret outputs. The results show that the IV-RF model achieves the highest predictive performance, while a stacking ensemble further reduces uncertainty. Sensitivity analyses confirm that model outcomes remain stable across random data splits and repeated non-flood point selections. High-risk areas are primarily located in the Hetao Plain, the Weihe River Basin, and sections of the lower Yellow River Basin, where susceptibility is driven by drainage density, anthropogenic and urban–mining soils, a high topographic wetness index, and gentle slopes. This work provides a transferable methodology that enhances physically consistent sample selection and model interpretability for flood risk assessment. Full article

Calligonum polygonoides is a rare desert shrub that is of high ecological importance in Egypt, yet its habitat requirements and physiological responses to environmental variability remain poorly understood, particularly under increasing climatic and anthropogenic pressures. To address this knowledge gap, this study integrates GIS-based habitat suitability modeling with hyperspectral leaf spectroscopy to evaluate the environmental factors associated with species distribution and leaf optical responses across two ecologically contrasting wadis (Wadi El-Galala and Wadi El-Assiuty). Environmental layers (DEM, EC, TSS, pH, temperature, rainfall, humidity, evaporation) were integrated using a multi-criteria evaluation framework, while plant cover, density, and spectral measurements were collected from 14 field plots. The results show that C. polygonoides favors moderately elevated zones characterized by low salinity, slightly alkaline soils, and intermediate climatic conditions. Approximately 24.3% of the landscape was classified as highly suitable, primarily along channel belts and alluvial fans with improved drainage and reduced salt accumulation. Spectral signatures showed descriptive differences between the two wadis. Plants from Wadi El-Galala showed relatively higher NIR reflectance and more pronounced SWIR water-absorption features compared with Wadi El-Assiuty, which may reflect differences in leaf structure and water status under contrasting habitat conditions. These spectral patterns were broadly consistent with the spatial suitability outputs and provide complementary descriptive information on leaf optical properties. Habitat suitability modeling showed that 91.7% of the recorded field occurrence points (33 out of 36 shrubs) were located within high-suitability zones, while no occurrences were recorded in low-suitability areas. This pattern indicates spatial agreement between observed occurrences and predicted suitability classes, but it should not be interpreted as formal model validation because absence data and independent validation records were not available. Overall, the findings provide preliminary spatial and spectral information that may support future field verification and site-specific conservation planning for C. polygonoides in the investigated wadis. Full article

Improving the flood adaptability of urban waterfront spaces is an essential entry point for enhancing regional stormwater regulation capacity, scientifically preventing flood disasters, and stabilizing urban water security. Existing flood adaptability assessments mostly rely on single weighting methods and individual evaluation models, inevitably causing systematic bias and low result robustness. Against this limitation, this study integrates remote sensing intelligent interpretation, spatiotemporal landscape pattern analysis, and multi-criteria decision theory to construct a comprehensive flood adaptability evaluation system under the pressure–state–response (PSR) framework. Innovatively, a game-theoretic combined weighting scheme integrating the entropy weight method, CRITIC method, and standard deviation method is proposed, and three complementary models including TOPSIS, VIKOR, and EDAS are coupled for cross-verification evaluation, which effectively improves the objectivity and robustness of spatial flood adaptability quantification. Taking Anqing City as a typical case, this study adopts Sentinel-2 time-series remote sensing images from 2016 to 2023 and applies an optimized random forest algorithm to automatically classify land cover. Five underlying surface types, including water bodies, vegetation, farmland, built-up areas, and bare land, are accurately extracted with an overall classification accuracy of around 90% for most years. Core landscape metrics such as Shannon’s diversity index and patch density are selected to systematically analyze the spatiotemporal differentiation characteristics of waterfront landscape patterns during the study period. The results indicate the obvious spatial heterogeneity of flood adaptability in Anqing City. Yingjiang District and Yuexi County present high comprehensive flood adaptability, while Wangjiang County and Huaining County show relatively low performance. Urban areas gain strong flood resistance from complete disaster prevention infrastructures and economic resilience; mountainous areas possess natural advantages in flood retention and drainage due to high vegetation coverage and topographic relief; by contrast, plain districts are severely restricted by low-lying terrain and insufficient drainage systems, resulting in prominent flood vulnerability. The proposed method is helpful for providing reliable scientific support for waterfront landscape optimization, zoned flood disaster management, and resilient water space planning in riverine cities. Full article

Child-well stimulation design has become a central challenge in mature unconventional reservoirs, where infill wells are commonly completed in reservoirs that have already been modified by parent-well production. Prior depletion changes pore pressure, stress distribution, and fracture-propagation pathways, causing child-well treatments to behave differently from parent-well completions. As a result, increasing fluid volume, proppant loading, stage density, or pump rate does not necessarily produce proportional gains in recovery. This review synthesizes the comprehensive literature on child-well stimulation intensity with emphasis on well performance, fracture-driven interactions, pad-scale economics, diagnostics, and resource-use considerations. The analysis shows that the production response is highly conditional: larger treatments can enhance reservoir contact when fractures access underdrained rock; however they may lose effectiveness when depletion-induced stress changes redirect fracture growth toward parent-well drainage areas or pre-existing fracture networks. In such cases, higher nominal intensity can increase interwell communication, reduce completion efficiency, impair parent-well performance, and weaken pad-level economic value. A key outcome of this review is the distinction between nominal stimulation intensity, represented by the treatment pumped, and effective stimulation intensity, represented by the fraction of that treatment that creates incremental productive fracture area. This distinction reframes child-well optimization from a treatment-size problem to a depletion-aware fracture-placement problem. Diagnostics, coupled modeling, production analysis, and mitigation strategies are therefore necessary to determine whether added stimulation intensity improves recovery or primarily redistributes production within the pad. From an economic perspective, the pad rather than the individual child well is the correct unit for evaluating stimulation-intensity decisions, since pad-level net present value integrates incremental child-well recovery, parent-well degradation, protection costs, spacing effects, and completion capital. Produced-water reuse and lifecycle emission benchmarking represent practical tools for reducing the environmental footprint of child-well development programs while simultaneously lowering freshwater demand and disposal volumes. These economic and environmental dimensions are inseparable from the technical optimization of stimulation intensity and are addressed explicitly in this review. This review concludes that child-well stimulation intensity should be optimized within a pad-scale framework that integrates depletion state, spacing, landing-zone selection, parent-well management, and long-term value rather than being uniformly maximized. Full article

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management. Full article

Urban ground collapse (UGC) threatens dense coastal cities because failures can occur abruptly beneath roads and buried lifelines. We developed a dynamic and interpretable machine-learning framework for UGC susceptibility assessment in Shenzhen, China. We integrated 1687 events from 2017 to 2024 with 20 predictors covering terrain, rainfall, drainage infrastructure and urban disturbance. Event-date rainfall was assigned to collapse samples, and background controls received year-constrained pseudo-event dates after spatial exclusion. Three tree-ensemble models were evaluated under random 75/25 testing, leave-one-district-out spatial validation and temporal hold-out testing. The selected LightGBM 1:5 model achieved ROC-AUC = 0.934, AP = 0.790 and BA = 0.860 in random testing, with more conservative ROC-AUC values of 0.905 and 0.884 under spatial and temporal validation. High-rainfall mapping expanded high and very high susceptibility zones from 4.60% to 15.26% and increased event capture from 19.32% to 58.92%. Grouped SHAP indicated comparable pipeline and rainfall contributions (26.01% and 25.13%). The leading predictors were pipe burial depth, road density and 30-day rainfall. PDP and spatial SHAP diagnostics highlighted high model responses where deep pipes coincided with high antecedent rainfall or dense roads in mature urban cores. This framework supports the rainfall-conditioned, mechanism-informed susceptibility diagnosis for early warning and mitigation. Full article

Identifying inflow and infiltration (I/I) in urban sewer networks is challenging due to nonlinear hydraulic interactions and time-lag effects between river stages and pipeline water levels. This study proposes a multi-scale fusion correlation model integrating Dynamic Time Warping (DTW), Pearson correlation, and Spearman rank correlation coefficients. The framework evaluates water level sequences across temporal windows (2, 6, and 12 h) under flexible displacement constraints (1 and 2 h), utilizing a dynamic weight-allocation mechanism based on sequence volatility and data density. Leveraging a 12-month monitoring dataset from 513 sensing devices in Zhongshan City, China, the model was evaluated on 36 typical water-level sequences. It achieved a classification accuracy of 91.7% for low-correlation sequences and perfect accuracy (100%) for both medium- and high-correlation levels. Furthermore, practical deployment in the Shaxi–Qijiang Highway section and Yicheng Area successfully isolated multiple vulnerable pipe segments suffering from Baishiyong River (tidal) water intrusion, yielding an empirical field-verification hit rate of 83.3%. The results demonstrate that the proposed framework effectively overcomes temporal asynchrony and nonlinear hydraulic noise, providing a robust, data-driven diagnostic tool for urban drainage infrastructures. Full article

Urban flood resilience depends not only on the scale of infrastructure investment, but also on whether such investment can be translated into observable flood-mitigation outcomes. Focusing on the transformation from infrastructure response to flood outcomes, this study uses panel data for 21 prefecture-level cities in Guangdong Province from 2016 to 2022. Annual maximum monthly precipitation is used to represent rainfall pressure, stormwater pipe density to represent infrastructure scale, and the number of reported flood events to represent the outcome variable. A two-way fixed-effects Poisson pseudo-maximum likelihood (PPML) model is employed. The results show that, in the full sample, rainfall pressure is positively, but not significantly, associated with reported flood occurrence, while stormwater pipe density does not exhibit a stable negative moderating effect. The main conclusion remains broadly unchanged when alternative outcome and precipitation indicators are used, when pipe density is lagged by one period, and when a conservative sample is adopted. Extended analysis provides only limited weak negative evidence for Pearl River Delta cities, and this evidence is not robust across alternative specifications. The findings indicate that pipe length per unit of built-up area primarily reflects the scale of infrastructure provision and cannot be directly equated with the operational performance of the drainage system. By separating response inputs from outcome performance, this study reveals the conditional nature of the transformation from infrastructure scale to operational performance in urban flood resilience research and provides empirical support for a shift from infrastructure expansion toward performance-oriented and integrated governance in high-density coastal cities. Full article

To address the weak interfacial transition zone and insufficient mechanical properties of recycled aggregate pervious concrete, this study proposes a dual modification strategy using basalt fibers and ultra-fine mineral powder. The macroscopic mechanical and hydraulic properties of the material were analyzed through orthogonal experiments. Techniques including X-ray diffraction, scanning electron microscopy, and micro-computed tomography were employed to systematically reveal the microstructural evolution and internal pore network topology of the modified system. Based on range analysis of mechanical stiffness and drainage efficiency, the optimal mix proportions were determined as 5–10 mm aggregate, a water–cement ratio of 0.31, and a fiber content of 0.50%. Microscopic tests confirm that the pozzolanic reaction of ultra-fine mineral powder increases matrix density and enhances the shear bond strength between fibers and the cement paste, enabling the physical bridging effect of basalt fibers. The dual modification exhibits a synergistic effect on load-bearing capacity and crack resistance. CT scan results show that the internal pore cross-sectional area follows a unimodal skewed distribution, with the characteristic distribution peak located at 3.5 mm². This homogeneous microporous network limits the critical defect size, optimizing the stress transfer path while ensuring fluid transport. Full article

Groundwater-potential assessment in karst aquifers is complicated by pronounced spatial heterogeneity driven by structural permeability, lithological variability, recharge redistribution, and unresolved subsurface conduit connectivity. Although machine-learning approaches have improved regional groundwater mapping, most existing models provide only deterministic predictions and offer limited information on predictive uncertainty and hydrogeological reliability. To address this limitation, we propose CT-TreeFlow. This probabilistic groundwater assessment framework goes beyond conventional machine-learning models by explicitly learning the full conditional probability distribution of groundwater favourability rather than a single deterministic estimate. The framework integrates sparse probabilistic environmental routing, conditional density estimation, hydrogeologically constrained pseudo-absence generation, geographically structured spatial validation, and explainability-driven interpretation within a unified modelling architecture, enabling simultaneous groundwater prediction, uncertainty quantification, and hydrogeological interpretation. The framework was applied to the Zagros karst system in Khuzestan Province, Iran, using remote-sensing-derived environmental predictors, Copernicus DEM-based morphometric variables, geological–structural datasets, and hydroclimatic indicators. Performance was evaluated against LightGBM and XGBoost using GroupKFold spatial cross-validation. CT-TreeFlow achieved a mean RMSE of 2.737 and a mean R² of 0.852, while also providing spatially explicit uncertainty estimates and probabilistic prediction intervals. Explainability analyses identified fracture density, lithology, drainage organisation, and terrain-controlled recharge conditions as the dominant controls on groundwater favourability. Predicted high-favourability zones showed strong spatial correspondence with major carbonate formations and independent spring–cave inventories, supporting the hydrogeological plausibility of the mapped patterns. These results demonstrate that probabilistic modelling can provide more reliable and physically interpretable groundwater assessments than deterministic approaches in structurally complex karst environments. CT-TreeFlow offers a transferable framework for uncertainty-aware groundwater exploration and regional hydrogeological decision support in heterogeneous aquifer systems. Full article

In arid and semi-arid regions, groundwater recharge is a key process controlling the sustainability of subsurface water resources. This study aims to assess and map the groundwater recharge potential of the Oued Cherrat watershed (Morocco) using an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). Six controlling factors were considered: lithology, lineament density, drainage network density, slope, land use/land cover derived from the Normalized Difference Vegetation Index (NDVI), and rainfall. The relative weights of these factors were determined through pairwise comparisons using the Saaty fundamental scale, and the consistency of the judgments was verified (CR < 0.1). The reclassified thematic layers were integrated into a GIS-based weighted overlay model to generate the groundwater recharge potential map. Five recharge classes were identified, ranging from very low to very high. The results show that areas with moderate recharge potential are the most widespread (approximately 37% of the watershed), while high and very high potential zones account for about 25% of the total area. These zones are mainly associated with permeable lithologies, high densities of structural discontinuities, gentle slopes, and low drainage density. In contrast, low to very low recharge potential areas are related to low-permeability formations, steep slopes, and dense drainage networks. The resulting recharge potential map provides a useful decision-support tool for sustainable groundwater management and for identifying priority areas for aquifer protection and artificial recharge planning in the Oued Cherrat watershed. Full article

Flash flood events are among the most critical hydrological hazards in arid and semi-arid regions, posing extreme threats to critical infrastructure, human safety, and sustainable development plans. This paper evaluates the flash flood susceptibility of the Al’Ataya catchment, a key watershed on the southern Red Sea coast, using an integrated geospatial analysis approach. To assess and quantify the flood hazard, we investigated 15 morphometric parameters for 24 particular sub-catchments within a sixth-order drainage system. Two complementary methods, the Morphometric Ranking Method and El-Shamy’s approach, were utilized to classify the catchment into different flood susceptibility levels. Results from the Ranking Method identified seven sub-catchments (SC-2, SC-3, SC-6, SC-7, SC-8, SC-9, and SC-19) as having high flood hazard levels, mainly driven by large watershed areas, steep slopes, and high relief ratios. In contrast, El-Shamy’s approach resulted in a different evaluation, identifying sub-catchments in Zone B (SC-23, SC-16, SC-17, SC-15, SC-6, SC-20) as high hazard sub-catchments due to the particular relationship between the bifurcation ratio parameter and the drainage density and stream frequency parameters. The integration of the two methods suggests that the susceptibility factor is controlled by the combined influence of a low drainage density and steep mountainous terrain draining toward the coastal zone. These results provide a spatial model for flood mitigation and early warning systems, supporting Saudi Vision 2030 through improvement to the development of southern urban centers such as Al’Ataya and Sabya. Full article

Intensive duck production is shifting from greenhouse/curtain-sided houses toward closed, mechanically ventilated systems, yet low-carbon environmental control for moisture-dominated houses remains insufficiently synthesized. Using the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) framework, this review aimed to identify low-carbon environmental-control pathways by integrating evidence on envelope design, ventilation, heat pump, and moisture management. Scopus, Web of Science, and PubMed were searched for English-language articles published during 2018–2025. Direct duck house evidence was separated from transferable poultry, livestock-building, and building-energy evidence. Synthesis shows that water access, wet litter, stocking density, and climate make houses latent-load-dominated systems, affecting relative humidity (RH), ammonia (NH₃), particulates, heat stress, welfare, and energy demand. Greenhouse-type houses have low energy use but weak environmental stability, whereas closed/windowless houses improve control and biosecurity but increase dependence on electricity, dehumidification, and backup systems. Low-carbon housing requires staged integration of moisture-source control, drainage, litter management, roof solar-load reduction, controlled ventilation, heat recovery, climate-suitable heat pumps, renewable electricity, sensor-based control, and resilience planning. Low-carbon environmental-control packages should be selected according to house type, climate, and management conditions. Future validation should report standardized energy, carbon, air quality, litter condition, welfare, productivity, cost, and outage-resilience metrics. Full article

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

The article deals with non-destructive methodologies for assessing and preventing corrosion of polymer-coated underground pipelines, advanced corrosion-barrier coating systems based on extruded three-layer high-density polyethylene (3LPE), corrosion control strategies for buried oil, gas, and water transmission infrastructures, and mechanisms and engineering approaches for corrosion prevention and mitigation. The quality assurance of newly polymer-coated underground pipelines, following construction (installation and backfilling), is vital for evaluating the polymer coating quality state and the efficiency of passive anti-corrosion protection, aimed at reducing corrosion risks and prolonging the pipeline’s service life. The evaluation relies on the coating average specific electrical resistance and the presence of coating defects (number, total area, and distribution) of inspected pipeline sections. In this study, based on extensive real data obtained from testing of newly installed underground water and oil/gas pipeline networks (60 projects with a total pipeline length of 260 km) with various technical characteristics, Drainage Test and DCVG (Direct Current Voltage Gradient) complementary non-destructive indirect methods have been investigated to determine the quality level and identify the location and severity of defects in polyolefin (polyethylene) coatings. The novel concepts and criteria were defined: the quantitative criteria for average specific electrical resistance are established; in addition, a new parameter related to the specific coating defects ratio is introduced, which has been shown to correlate with the criteria for the average specific electrical resistance of the polymer coating and consumed electrical current; finally, following DCVG measurements of the 3LPE coating system, a novel degree of relative defect sizes (%IR) for repairs has been suggested. The innovative and comprehensive approach can support the efforts of regulatory quality assurance, design, maintenance, safety, and research communities to ensure the long-term integrity and sustainability of underground polymer-coated steel pipelines. Full article