Search Results (45)

Groundwater is the primary source of irrigation in many semi-arid hard-rock aquifer regions. Yet, its suitability assessment is often hindered by the nonlinear hydrochemical dynamics that traditional bivariate tools, such as the U.S. Salinity Laboratory (USSL) diagram, cannot adequately resolve. To overcome this limitation, we developed an explainable artificial intelligence (XAI) framework that predicts irrigation suitability categories directly from hydrochemical variables, without relying on calculated indices. Using 1872 post-monsoon groundwater samples from Telangana, India, we trained three ensemble tree-based classifiers (Random Forest, LightGBM, and XGBoost) on 11 hydrochemical variables (Na⁺, K⁺, Ca²⁺, Mg²⁺, HCO₃⁻, Cl⁻, F⁻, NO₃⁻, SO₄²⁻, pH, and total hardness). Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and model hyperparameters were optimized with Optuna. Among the tested models, LightGBM achieved the best performance (balanced accuracy = 0.938). Model interpretability was enabled using Shapley Additive Explanations (SHAP), supported by Piper and Gibbs diagrams, revealing a critical distinction between sodicity-driven salinity and hardness-driven mineralization, identifying calcium-saturated waters for which gypsum amendment can be chemically futile. To bridge the gap between algorithmic accuracy and operational simplicity, we distilled SHAP explanations into linear heuristics and quantified the trade-off between accuracy and simplicity. Accordingly, we proposed a tiered hydrochemical triage framework in which quantitative heuristics handled approximately 62.5% of the routine samples, while XAI resolved the complex and ambiguous cases. Overall, the proposed framework transforms classic suitability assessment tools into an adaptable, evidence-informed, proactive decision-support system for sustainable agricultural water management under increasing environmental stress. Full article

Groundwater quality in the semi-arid region of Oum El Bouaghi, Northeastern Algeria, was assessed for irrigation suitability using hydrogeochemical analyses, water quality indices, and GIS techniques. The study analyzed 23 groundwater samples during dry and wet seasons in 2022–2023, several physicochemical parameters were measured. Results revealed neutral to slightly alkaline pH levels, except for one acidic sample, with salinity (EC: 527–5001 µS·cm⁻¹) exceeding WHO guidelines, particularly during the dry season due to evaporation and anthropogenic activities. Hydrogeochemical facies showed dominance of Na⁺-HCO₃⁻ and Ca²⁺-Cl⁻/SO₄²⁻ water types, indicating rock–water interactions and evaporation control, as confirmed by Gibbs plots. The IWQI classified water into five categories, with severe restrictions (IWQI < 40) in 13% of samples during the dry season, improving slightly in the wet season. Indices such as SAR, Na%, and RSC indicated low to moderate sodium hazard, while KR and PS highlighted salinity risks in specific areas. Spatial analysis revealed localized pollution hotspots, with the (GPI) identifying minimal to high contamination levels, linked to agricultural and geogenic sources. These findings underscore needs for sustainable groundwater management, including monitoring, optimized irrigation practices, and mitigation of anthropogenic impacts, to ensure long-term agricultural viability in this water-scarce region. Full article

The rise in sea levels due to global warming and the excessive extraction of groundwater in coastal regions significantly encourages seawater intrusion, resulting in a cascade of ecological and environmental issues, including water quality degradation and soil salinization. The northern sector of Laizhou City, situated on the eastern coast of Laizhou Bay, exemplifies a typical location of seawater intrusion in China, where the rising salinity of groundwater has adversely affected local economic development and public health. This investigation involved the collection of 115 groundwater samples and 13 isotope samples from the northern region of Laizhou City. Statistical analysis, Piper’s trilinear diagrams, and various analytical techniques were employed to examine the chemical properties of the groundwater in the study area; characteristic ion ratios, Gibbs diagram, and hydrogen–oxygen isotope methods were utilized to analyze the sources of salinity and groundwater recharge; and a seawater intrusion groundwater quality index, which was applied to the present condition of seawater intrusion, was assessed utilizing the seawater intrusion groundwater quality index (GQI_SWI). The findings indicate that the chemical composition of groundwater in the research area is notably intricate. From freshwater to saline water, the groundwater chemistry transitions from Ca-HCO₃·Cl-type water to Ca·Na-SO₄·Cl-type water, and finally to Na-Cl-type water. Seawater intrusion in the research area is the primary cause of elevated groundwater salinity, alongside cation exchange and water–rock interactions that affect water chemistry. Seawater intrusion is predominantly focused in the northern region of the research area. The primary source of groundwater recharge is atmospheric precipitation. Full article

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article

Groundwater plays a leading role in ecological environment protection in semi-arid regions. The Huangshui River Basin is located in the Tibetan Plateau and Loess Plateau transition zone of semi-arid areas. Its ecological environment is relatively fragile, and there is an urgent need for systematic study of the basin to develop a groundwater environment and realize the rational and efficient development of water resources. In this study, methodologically, we combined the following: 1. Field sampling (271 groundwater samples across the basin’s hydrogeological units); 2. Comprehensive laboratory analysis of major ions and physicochemical parameters; 3. Multivariate statistical analysis (Pearson correlation, descriptive statistics); 4. Geospatial techniques (ArcGIS kriging interpolation); 5. Hydrochemical modeling (Piper diagrams, Gibbs plots, PHREEQC simulations). Key findings reveal the following: 1. Groundwater is generally weakly alkaline (pH 6.94–8.91) with TDS ranging 155–10,387 mg/L; 2. Clear spatial trends: TDS and major ions (Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻) increase along flow paths; 3. Water types evolve from Ca-HCO₃-dominant (upper reaches) to complex Ca-SO₄/Ca-Cl mixtures (lower reaches); 4. Water–rock interactions dominate hydrochemical evolution, with secondary cation exchange effects; 5. PHREEQC modeling identifies dominant carbonate dissolution (mean SIcalcite = −0.32) with localized evaporite influences (SIgypsum up to 0.12). By combining theoretical calculations and experimental results, this study reveals distinct hydrochemical patterns and evolution mechanisms. The groundwater transitions from Ca-HCO₃-type in upstream areas to complex Ca-SO₄/Cl mixtures downstream, driven primarily by dissolution of gypsum and carbonate minerals. Total dissolved solids increase dramatically along flow paths (155–10,387 mg/L), with Na⁺ and SO₄²⁻ showing the strongest correlation to mineralization (r > 0.9). Cation exchange processes and anthropogenic inputs further modify water chemistry in midstream regions. These findings establish a baseline for sustainable groundwater management in this ecologically vulnerable basin. Full article

In this study, eco-friendly and sustainable alginate-activated carbon (Alg-C)-based beads were synthesized and characterized for the adsorption of nonylphenols (NPs) from aqueous environments under various conditions. The surface characterization, functional groups, and adsorption behavior were analyzed using multiple analytical techniques. The effect of key parameters, including dosage, pH, temperature, and reusability, were evaluated. Isotherm and kinetic studies revealed that the adsorption process followed a pseudo-second-order kinetic model and aligned with the Freundlich isotherm, indicating a heterogeneous surface. The beads exhibited a high removal efficiency of 97% over five reuse cycles in a 50 mL solution of 10 mg L⁻¹ NPs under static conditions, demonstrating their recyclability. Thermodynamic analysis suggested potential electrostatic interactions, supported by positive Gibbs free energy values. The highest removal performance was achieved within 90 min, with adsorption capacities from 0.10 to 0.39 mg g⁻¹. Additionally, the performance of Alg-C beads remained stable across different pH levels, highlighting their robustness. When tested with wastewater samples, Alg-C beads maintained high removal efficiency, with no significant matrix effects observed. These results underscore Alg-C beads as a promising and sustainable solution for the elimination of NPs from contaminated water sources. Full article

The hydrological dynamics of Raipur are profoundly influenced by the intricate interplay between surface and groundwater systems, driven by changes in land use, climatic conditions, and human activities such as agriculture and industry. This research investigated the interdependencies between the Kharun River and groundwater systems, essential for understanding water security in the face of escalating demands and rapid urbanization. Through meticulous monitoring and analysis of approximately 70 bore wells, nine river sampling sites, and 13 groundwater samples from dug wells, alongside rigorous adherence to established sampling protocols, this study delved into the seasonal variations and influences on water quantity and quality. Statistical methodologies, stable isotope analyses, and Gibbs diagrams were employed to unravel the complexities governing water resource dynamics and interactions. Notably, correlation analysis revealed significant associations between various water quality parameters, indicating anthropogenic influences on groundwater chemistry. Cluster analysis aided in understanding hydro-chemical processes, while stable isotope examinations further elucidated the sources and interactions of groundwater and surface water. Results indicate the urgent need for sustainable water management strategies tailored to the region’s evolving socio-environmental landscape, considering escalating urbanization and agricultural activities. This integrated approach, combining analytical methods and statistical techniques, offers a holistic understanding of water resource dynamics essential for effective governance and sustainable development. Full article

Groundwater serves as a vital water source in Zhanjiang City. This study analyzed the chemical components of 35 samples to understand the hydrochemical characteristics and evolution mechanism of groundwater in Zhanjiang City. A comprehensive analysis was conducted using statistical methods, including the use of Piper trilinear diagrams, the Gibbs method, ion ratios, and other techniques, to investigate the sources and control factors of the main ions in groundwater in the area. The findings reveal that all the groundwater is freshwater, with the main cations and anions being Na⁺ and HCO₃⁻, respectively. Shallow water is mainly of the Cl•SO₄ mixed cation type, followed by the HCO₃•ClNa•Ca type. Middle and deep pore water is mainly of the HCO₃Na type, followed by the HCO₃Na•Mg type and HCO₃Na•Ca type. The hydrochemical classifications of pore and fissure water are Cl•HCO₃Mg•Na type and Cl•HCO₃Na type water. The primary hydrogeochemical process is water–rock interaction, particularly the dissolution of silicate minerals. Additionally, evaporation and concentration contribute significantly to the chemical composition of shallow water, and ion exchange is also an important hydrogeochemical process affecting middle and deep pore water. Shallow water commonly contains nitrates, with 37.5% of shallow water showing contamination with NO₃⁻. This study aims to provide insights into the development and utilization of local water resources. Full article

This work presents a comprehensive approach for the justification of the reagent regime of coal flotation by investigating the interfacial characteristics of flotation phases with various techniques. For the energy characterization of the surface processes in flotation systems, a method of establishing the components of the specific surface Gibbs energy on the basis of a numerical estimation of surface free energy change during the adsorption of flotation reagents using the Owens–Wendt–Rabel–Kaelble technique was proposed. Using the developed approach, the features of the kinetics of n-hexane sorption on the surface of coal samples were established. The substantiation of differences in the potential mechanisms of the fixation of strictly apolar and aromatic reagents is based on the results of the quantum–chemical modeling of the states of the coal–adsorbate system using the software packages Avogadro and Orca. The simulation shows the possibility of aliphatic and aromatic reagents’ synergetic effects on coal surface hydrophobization. Based on the results of quantum–chemical modeling, it was found that for the physical adsorption of an oxyethylated nonyl-phenol molecule on a molecular fragment of the coal surface, according to the Weiser model, the decrease in the energy of the system was 0.05562 eV, which indicates the high thermodynamic probability of the physical sorption of this compound. The parameters of the Langmuir monomolecular model for the sorption of oxyethylated nonyl-phenol on the surface of the studied coal samples were established. The criterion characterizing the interphase phenomena in the flotation system based on the results of potentiometric studies of the interfacial characteristics, E_f, was proposed. It was found that for the studied values of the flow rate of oxyethylated nonyl-phenol, the highest value of E_f was achieved when the value of the sorption of the reagent equaled 63.99% of the limiting sorption capacity. The performance of the proposed reagents for coal flotation was confirmed by flotation tests. Full article

In pursuit of overcoming Fenton oxidation limitations in wastewater treatment, an introduction of a heterogeneous photocatalyst was developed. In this regard, the current work introduces ZnO nanocrystals that were successfully prepared via a thermal decomposition technique and then capped with oleic acid (OA). The synthesized ZnO-OA and the pristine ZnO were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FE-SEM). Then, the study introduces the application of such materials in advanced oxidation processes, i.e., a Fenton reaction to treat dye-containing wastewater. Synthetic wastewater that was prepared using Reactive Blue 4 (RB4) was used as a simulated textile wastewater effluent. Fenton’s oxidation was applied, and the system parameters were assessed using the modified Fenton’s system. The synthesized samples of ZnO were characterized by a recognized wurtzite hexagonal structure. The surface modification of ZnO with oleic acid (OA) resulted in an increase in crystallite size, lattice parameters, and cell volume. These modifications were linked to the efficient capping of ZnO nanoparticles by OA, which further improved the dispersion of the nanoparticles, as demonstrated through SEM imaging. The optimum conditions of ZnO- and ZnO-OA-synthesized modified Fenton composites showed 400 mg/L and 40 mg/L for H₂O₂ and the catalyst, respectively, at pH 3.0, and within 90 min under UV irradiation the maximal dye oxidation reached 93%. The catalytic performance at its optimal circumstances was in accordance with a pseudo-second-order kinetics model for both ZnO-OA- and the pristine ZnO-based Fenton’s systems. The thermodynamic parameters, including the enthalpy (ΔH′), the entropy (ΔS′), and Gibbs free energy (ΔG′) of activations, were also checked, and their values settled that both ZnO and ZnO-OA Fenton systems are non-spontaneous in nature. Furthermore, the reaction signified for processing at a low energy barrier condition (10.38 and 31.38 kJ/mol for ZnO-OA- and the pristine ZnO-based Fenton reactions, respectively). Full article

As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model. Full article

This paper proposes a semiparametric spatial lag model and develops a Bayesian estimation method for this model. In the estimation of the model, the paper combines Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, random walk Metropolis sampler, and Gibbs sampling techniques to sample all the parameters. The paper conducts numerical simulations to validate the proposed Bayesian estimation theory using a numerical example. The simulation results demonstrate satisfactory estimation performance of the parameter part and the fitting performance of the nonparametric function under different spatial weight matrix settings. Furthermore, the paper applies the constructed model and its estimation method to an empirical study on the relationship between economic growth and carbon emissions in China, illustrating the practical application value of the theoretical results. Full article

This study provides an in-depth analysis of the hydrochemical characteristics and their controlling factors in the lower reaches of the Yellow River. Through water quality sampling and analysis over two hydrological periods within a year, combined with hydrochemical methods and machine learning techniques, the study reveals the joint impact of natural factors and human activities on the spatiotemporal variations in hydrochemical constituents. The findings indicate that the water in the lower reaches of the Yellow River exhibits weak alkalinity (the pH is between 7 and 8), with the primary hydrochemical type being HCO₃·SO₄—Ca·Na·Mg. The temporal variation in the hydrochemical constituents is mainly influenced by rainfall, where nitrate levels are higher during the flood season due to the flushing effect of rainfall, whereas other hydrochemical constituents show an opposite temporal pattern due to the dilution effect of rainfall. The spatial variation in the Yellow River’s hydrochemistry is primarily controlled by a combination of human activities and rainfall. Using Gibbs diagram analysis, it is identified that rock weathering is the main source of ionic constituents, while agricultural fertilization, industrial emissions, and domestic wastewater discharge have significant impacts on the hydrochemical constituents. Compared to other rivers worldwide, the concentration of hydrochemical constituents in the lower reaches of the Yellow River is relatively high, especially nitrate and sulfate, which is closely related to the geological characteristics of the Yellow River basin and intense human activities in the middle and lower reaches. Principal component analysis reveals that the main controlling factors for hydrochemical constituents during the dry season in the lower reaches of the Yellow River are rock weathering dissolution and industrial activities, followed by domestic wastewater; during the flood season, the main controlling factors are rock weathering dissolution and industrial activities, followed by agricultural activities and domestic wastewater. The research findings provide theoretical support for water resource management and water quality protection in the lower reaches of the Yellow River. Full article

A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link prediction difficult, especially in the setting of complex relations. At the same time, current techniques that rely on distance models encapsulate entities within Euclidean space, limiting their ability to depict nuanced relationships and failing to capture their semantic importance. This research offers a unique strategy based on Gibbs sampling and connection embedding to improve the model’s competency in handling link prediction within complex relationships. Gibbs sampling is initially used to obtain high-quality negative samples. Following that, the triplet entities are mapped onto a hyperplane defined by the connection. This procedure produces complicated relationship embeddings loaded with semantic information. Through metric learning, this process produces complex relationship embeddings imbued with semantic meaning. Finally, the method’s effectiveness is demonstrated on three link prediction benchmark datasets FB15k-237, WN11RR and FB15k. Full article

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient. Full article