Search Results (498)

Shale gas, as an important unconventional hydrocarbon resource, has attracted much attention due to its great potential and the need for energy diversification. However, shale gas reservoirs with low permeability and low porosity pose challenges for extraction, making shale fracability evaluation crucial. Conventional methods have limitations as they cannot comprehensively consider the effects of non-linear factors or quantitatively analyse the effects of factors. In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). SHAP(SHapley Additive exPlanation) value analyses were used to quantitatively assess the factor impacts, provide the characteristic importance ranking, and visualise the contribution trend through summary and dependency plots. Analyses of seven scenarios showed that ‘Vertical—Min Horizontal’ and ‘Vertical Stress’ had the greatest impact. This approach improves the accuracy and interpretability of the assessment and provides strong support for shale gas exploration and development by enhancing the understanding of the role of factors. Full article

The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R² of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance. Full article

Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5). Full article

Background/Objectives: The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual tumor presence and early recurrence, is associated with poorer survival outcomes. To address this, we developed a prediction model to identify patients at a high risk of persistent tumor status prior to initiating treatment. Methods: This retrospective study included 104 patients with NPC receiving radiotherapy-related treatment who had completed a 3-year follow-up period; 29 were classified into the persistent tumor status group and 75 into the disease-free group. Radiomic features were extracted from pretreatment positron emission tomography (PET) images and used to construct a prediction model by employing machine learning algorithms. The model’s diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC), whereas SHapley Additive exPlanations (SHAP) analysis was conducted to determine the contribution of individual features to the model. Results: The prediction model developed using the AdaBoost algorithm and validated through five-fold cross-validation achieved the highest AUC of 0.934. Its sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 89.66%, 86.67%, 72.22%, 95.59%, and 87.5%, respectively. SHAP analysis revealed that the feature of high dependence low metabolic uptake emphasis₅₀ had the greatest impact on model predictions. Furthermore, patients classified as disease-free exhibited markedly higher overall survival rates compared with those with persistent tumor status. Conclusions: In conclusion, the proposed prediction model efficiently identified patients with NPC at a high risk of persistent tumor status by using radiomic features extracted from pretreatment PET images. Full article

Well-production forecasting plays a crucial role in oil and gas development. Traditional methods, such as numerical simulations, require substantial computational effort, while empirical models tend to exhibit poor accuracy. To address these issues, machine learning, a widely adopted artificial intelligence approach, is employed to develop production forecasting models in order to enhance the accuracy of oil and gas well-production predictions. This research focuses on the geological, engineering, and production data of 435 fracturing wells in the North China Oilfield. First, outliers were detected, and missing values were handled using the mean imputation and nearest neighbor methods. Subsequently, Pearson correlation coefficients were utilized to eliminate linearly irrelevant features and optimize the dataset. By calculating the gray correlation degrees, maximum mutual information, feature importance, and Shapley additive explanation (SHAP) values, an in-depth analysis of various dominant factors was conducted. To further assess the importance of these factors, the entropy weight method was employed. Ultimately, 19 features that were highly correlated with the target variable were successfully screened as inputs for subsequent models. Based on the AutoGluon framework, model training was conducted using 5-fold cross-validation combined with bagging and stacking techniques. The training results show that the model achieved an R² of 0.79 on the training set, indicating good fitting ability. This study offers a promising approach for the development of oil and gas production forecasting models. Full article

The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features. Full article

Background and Objectives: The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, but their long-term use raises concern about intraocular pressure (IOP). This study uses SHapley Additive exPlanations (SHAP) to improve the interpretability of machine learning (ML) model predicting end IOP, offering clinicians explainable insights for personalized patient management. Materials and Methods: This retrospective study analyzed data from 1191 individual eyes of 639 boys and 552 girls with myopia treated with atropine. The average age of the whole group was 10.6 ± 2.5 years old. The refractive error of spherical equivalent (SE) in myopia degree was base SE at 2.63D and end SE at 3.12D. Data were collected from clinical records, including demographic information, IOP measurements, and atropine treatment details. The patients were divided into two subgroups based on a baseline IOP of 14 mmHg. ML models, including Lasso, CART, XGB, and RF, were developed to predict the end IOP value. Then, the best-performing model was further interpreted using SHAP values. The SHAP module created a personalized and dynamic graphic to illustrate how various factors (e.g., age, sex, cumulative duration, and dosage of atropine treatment) affect the end IOP. Results: RF showed the best performance, with superior error metrics in both subgroups. The interpretation of RF with SHAP revealed that age and the recruitment duration of atropine consistently influenced IOP across subgroups, while other variables had varying effects. SHAP values also offer insights, helping clinicians understand how different factors contribute to predicted IOP value in individual children. Conclusions: SHAP provides an alternative approach to understand the factors affecting IOP in children with myopia treated with atropine. Its enhanced interpretability helps clinicians make informed decisions, improving the safety and efficacy of myopia management. This study demonstrates the potential of combining SHAP with ML models for personalized care in ophthalmology. Full article

The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. First, LF-NMR signals were obtained from eleven maize kernel varieties, and ten key features were extracted from the transverse relaxation decay curves. Meanwhile, five image morphological features were selected using the recursive feature elimination (RFE) algorithm. Before modeling, principal component analysis (PCA) was used to determine the distribution features of the internal components for each maize variety. Subsequently, LF-NMR features and image morphological data were integrated to construct a classification model and the SVM hyperparameters were optimized using an improved differential evolution algorithm, achieving a final classification accuracy of 96.36%, which demonstrated strong robustness and precision. The model’s interpretability was further enhanced using Shapley values, which revealed the contributions of key features such as Max Signal and Signal at Max Curvature to classification decisions. This study provides an innovative technical solution for the efficient identification of maize varieties, supports the refined management of germplasm resources, and lays a foundation for genetic improvement and agricultural applications. Full article

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R² (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture. Full article

With the Multi-Criteria Decision-Making (MCDM) problems becoming increasingly complex, traditional MCDM methods cannot effectively handle ambiguous, incomplete, or uncertain data. While several novel types of MCDM methods have been proposed to address this limitation, they fail to consider the potentially complex interactions among decision criteria. An effective capacity identification methodology is definitely needed to conquer this issue. In this paper, we develop a novel unsupervised method for identifying 2-additive capacities by means of Principal Component Analysis (PCA) and Kendall’s correlation coefficient. During the process, some significant results are achieved. Firstly, the Shapley values of decision criteria are derived by using the PCA, through a combination of the variance contribution rate of each Principal Component (PC) and its corresponding eigenvector. Secondly, Kendall’s correlation coefficient stemmed from the decision data created to help identify the Shapley interaction index for each pair of criteria by unsupervised learning. The optimization model equipped with a new form of monotonicity conditions is then established to further determine the optimal Shapley interaction index. With these two kinds of indices, a desired monotone 2-additive capacity is finally identified in an objective and efficient manner. Numerical experiments demonstrate that our proposal can adequately consider the importance of criteria and accurately identify the types of Shapley interaction indices between criteria, and is thus able to produce more convincing and logical results compared with other unsupervised identification methods. Full article

The implementation of accurate water balance assessment in glacier basins is essential for the management and sustainable development of water resources in the basins. In this study, a hybrid modeling framework was constructed to enhance runoff prediction and water balance assessment in glacier basins. An improved physical hydrological model (SEGSWAT+) was combined with a machine learning model (ML) to capture the relationship between runoff residuals and water balance components through the Shapley additive explanations (SHAP) method. Based on the enhancement of the runoff fitting results of the existing model, the runoff residuals are decomposed and used to correct the hydrological process component values, thus improving the accuracy of the water balance results. We evaluated the performance and correction results of the method using various ML methods. We analyzed the results for two consecutive periods from 1959 to 2022 for the glacial sub-basins of three tributaries of the Upper Ili River Basin in central Asia. The results show that the hybrid framework based on extreme gradient boosting (XGBoost) with an average NSE value of 0.93 has the best performance, and the bias based on the evapotranspiration component and soil water content change component is reduced by 3.2–5%, proving the effectiveness of the water balance correction. This study advances the interpretation of ML models for hydrologic assessment of areas with complex hydrodynamic characteristics. Full article

The splitting tensile strength of concrete is crucial for structural integrity, as tensile stresses from load and environmental changes often lead to cracking. This study investigates the effectiveness of advanced ensemble machine-learning models, including LightGBM, GBRT, XGBoost, and AdaBoost, in accurately predicting the splitting tensile strength of silica fume-enhanced concrete. Using a robust database split into training (80%) and testing (20%) sets, we assessed model performance through R², RMSE, and MAE metrics. Results demonstrate that GBRT and XGBoost achieved superior predictive accuracy, with R² scores reaching 0.999 in training and high precision in testing (XGBoost: R² = 0.965, RMSE = 0.337; GBRT: R² = 0.955, RMSE = 0.381), surpassing both LightGBM and AdaBoost. This study highlights GBRT and XGBoost as reliable, efficient alternatives to traditional testing methods, offering substantial time and cost savings. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted to identify key input features and to elucidate their influence on splitting tensile strength, providing valuable insights into the predictive behavior of silica fume-enhanced concrete. The SHAP analysis reveals that the water-to-binder ratio and curing duration are the most critical factors influencing the splitting tensile strength of silica fume concrete. Full article

The technical complexity of organizing energy systems’ operation has recently been compounded by the complexity of reconciling the interests of individual entities involved in interactions. This study proposes a possible solution to the problem of modeling their relationships within a large system. Our solution takes into account multiple levels of interactions, imperfect information, and conflicting interests. We present a mathematical statement of the problem of optimal interactions between the centralized system and prosumers in the integrated energy system (IES) with due consideration of the layered architecture of the IES. The paper also contributes a model for arranging the interactions between centralized and distributed energy sources for cases when IES prosumers form coalitions. The implementation of this model is based on multi-agent techniques and cooperative game theory tools. In order to arrive at a rational arrangement of the interactions of prosumers in the IES, the model implements different approaches to the allocation of the coalition’s total payoff (the Shapley value, Modiclus, PreNucleolus solution concepts). Furthermore, we propose a criterion for deciding on the “best” imputation. We contribute a multi-agent system that implements the proposed model and use a test IES setup to validate the model by simulations. The results of the simulations ensure optimal interactions between the entities involved in the energy supply process within the IES and driven by their own interests. The results also elucidate the conditions that make it feasible for prosumers to form coalitions. Full article

Urbanization intensifies the need for sustainable public transportation that balances financial viability, environmental sustainability, and social equity. Traditional fare-setting methods often focus narrowly on financial objectives, neglecting broader impacts. This study introduces a novel collaborative game-theoretic model integrating user sentiment analysis to optimize fare policies. By incorporating utilities of passengers, operators, and governments, and employing the Shapley value for fair benefit distribution, this model aims to maximize social welfare. The methodology frames fare optimization as a cooperative game among stakeholders, integrating passenger preferences through sentiment analysis. The social welfare function combines the utilities of all stakeholders and is maximized under operational, environmental, and financial constraints. Implemented in Python and applied to Isparta, Turkey, the model identifies an optimal fare of 19.5 TL (ranged between 14 and 26.50 TL) that maximizes social welfare, aligning closely with existing fares. Shapley value analysis distributes the benefits, assigning 221,457 (35.6%) units to passengers, 54,562 (8.7%) units to operators, and 347,433 (55.7%) units to the government, highlighting significant environmental gains for the government. Sensitivity analyses confirm the model’s robustness across varying trip volumes, suggesting its applicability to diverse urban settings. This research contributes to socially equitable and user-centric fare policies by providing a comprehensive framework aligning stakeholder interests. Policymakers can leverage this model to design fare strategies promoting sustainability, efficiency, and collaboration in public transportation systems. Full article

The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential for water resource management and sustainable agricultural development. However, natural factors introduce uncertainty and result in poor alignment when predicting farmland SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include the largest patch index (LPI), edge density (ED), aggregation index (AI), patch cohesion index (COH), contagion index (CON), landscape division index (DIV), percentage of like adjacencies (PLA), Shannon evenness index (SHEI), and Shannon diversity index (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed to leverage these indices for predicting the spatial variability of SWC. Statistical analysis revealed that landscape indices exhibited skewed distributions and weak linear correlations with SWC (r < 0.2). Despite this, incorporating landscape index variables into the BO–DF model significantly improved prediction accuracy, with R² increasing by 35.85%. This model demonstrated a robust nonlinear fitting capability for the spatial variability of SWC. Spatial mapping of SWC using the BO–DF model indicated that high-value areas were predominantly located in the eastern and southern regions of the Yellow River Delta in China. Furthermore, the SHapley additive explanation (SHAP) analysis highlighted that landscape indices were key drivers in predicting SWC. These findings underscore the potential of landscape indices as valuable variables for spatial SWC prediction, supporting regional strategies for sustainable agricultural development. Full article