Explore the vast range of titles available.

Forecasting July precipitation using prophase winter sea surface temperature through a nonlinear machine learning model remains challenging. Given the scarcity of observed samples and more attention should be paid to anomalous precipitation events, the shallow neural network (NN) and several improving techniques are employed to establish the statistical forecasting system. To enhance the stability of predicted precipitation, the final output precipitation is an ensemble of multiple NN models with optimal initial seeds. The precipitation data from anomalous years are amplified to focus on anomalous events rather than normal events. Some artificial samples are created based on the relevant background theory to mitigate the problem of insufficient sample size for model training. Sensitivity experiments indicate that the above techniques could improve the stability and interpretability of the forecasting system. Rolling forecasts further indicate that the forecasting system is robust and half of the anomalous events can be successfully predicted. These improving techniques used in this study can be applied not only to the precipitation over the middle-lower reaches of the Yangtze River but also to other climate events.

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property. However, traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction, while a single structural model cannot handle input sequences of different lengths at the same time. In order to solve these limitations, in this study, a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient (MIC), long short-term memory network (LSTM), and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction. Through MIC, a suitable input sequence length is selected for the LSTM model. To investigate the influence of rainfall on landslides during different seasons, a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths. The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration (CMA) and Reliable Prognosis ( https://rp5.ru ) to improve the model’s expression capability, and the student model learns sequence information from other time series. An attention module is then designed to integrate different sequence information to derive a context vector, representing seasonal temporal attention mode. Finally, the predicted displacement is obtained through a linear layer. The proposed method demonstrates superior prediction accuracies, surpassing those of the support vector machine (SVM), LSTM, recurrent neural network (RNN), temporal convolutional network (TCN), and LSTM-Attention models. It achieves a mean absolute error (MAE) of 0.072 mm, root mean square error (RMSE) of 0.096 mm, and pearson correlation coefficients (PCCS) of 0.85. Additionally, it exhibits enhanced prediction stability and interpretability, rendering it an indispensable tool for landslide disaster prevention and mitigation.

Landslide susceptibility mapping is a crucial tool for disaster prevention and management. The performance of conventional data-driven model is greatly influenced by the quality of the samples data. The random selection of negative samples results in the lack of interpretability throughout the assessment process. To address this limitation and construct a high-quality negative samples database, this study introduces a physics-informed machine learning approach, combining the random forest model with Scoops 3D, to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area. The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method. Instead of conventional random selection, negative samples are extracted from the areas with a high factor of safety value. Subsequently, the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed, focusing on model performance and prediction uncertainty. In comparison to conventional methods, the physics-informed model, set with a safety area threshold of 3, demonstrates a noteworthy improvement in the mean AUC value by 36.7%, coupled with a reduced prediction uncertainty. It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.

Regarding the interpretable techniques in the field of image recognition, Grad-CAM is widely used for feature localization in images to reflect the logical decision-making information behind the neural network due to its high applicability. However, extensive experimentation on a customized dataset revealed that the deep convolutional neural network (CNN) model based on Gradient-weighted Class Activation Mapping (Grad-CAM) technology cannot effectively resist the interference of large-scale noise. In this article, an optimization of the deep CNN model was proposed by incorporating the Dropkey and Dropout (as a comparison) algorithm. Compared with Grad-CAM, the improved Grad-CAM based on Dropkey applies an attention mechanism to the feature map before calculating the gradient, which can introduce randomness and eliminate some areas by applying a mask to the attention score. Experimental results show that the optimized Grad-CAM deep CNN model based on the Dropkey algorithm can effectively resist large-scale noise interference and achieve accurate localization of image features. For instance, under the interference of a noise variance of 0.6, the Dropkey-enhanced ResNet50 model achieves a confidence level of 0.878 in predicting results, while the other two models exhibit confidence levels of 0.766 and 0.481, respectively. Moreover, it exhibits excellent performance in visualizing tasks related to image features such as distortion, low contrast, and small object characteristics. Furthermore, it has promising prospects in practical computer vision applications. For instance, in the field of autonomous driving, it can assist in verifying whether deep learning models accurately understand and process crucial objects, road signs, pedestrians, or other elements in the environment.

Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict employee attrition and implement effective retention strategies. This paper delves into the application of explainable AI (XAI) in identifying potential employee turnover and devising data-driven solutions to address this complex problem. The first part of the paper examines the escalating problem of employee attrition in specific industries, analyzing the detrimental impact on organizational productivity, morale, and financial stability. The second section focuses on the utilization of AI techniques to predict employee attrition. AI can analyze historical data, employee behavior, and various external factors to forecast the likelihood of an employee leaving an organization. By identifying early warning signs, businesses can intervene proactively and implement personalized retention efforts. The third part introduces explainable AI techniques which enhance the transparency and interpretability of AI models. By incorporating these methods into AI-based predictive systems, organizations gain deeper insights into the factors driving employee turnover. This interpretability enables human resources (HR) professionals and decision-makers to understand the model’s predictions and facilitates the development of targeted retention and recruitment strategies that align with individual employee needs.

Machine learning (ML) algorithms have been widely applied to analyze geotechnical engineering problems due to recent advances in data science. However, flexible ML models trained with limited data can exhibit unexpected behaviors, leading to low interpretability and physical inconsistency, thus, reducing the reliability and robustness of ML models for risk forecasting and engineering applications. As input features for geotechnical engineering applications often represent physical parameters following intrinsic and often monotonic relationships, incorporating monotonicity into ML models can help ensure the physical realism of model outputs. In this study, monotonicity was introduced as a soft constraint into artificial neural network (ANN) models, and their results were compared with several benchmark ML models. During the training process, data augmentation and point-wise gradient were used to evaluate the monotonicity of model predictions, and monotonicity violations were minimized through a modified loss function. A compilation of slope stability case histories from the literature was used for model development, benchmarking their performance, and evaluating the effects of monotonicity constraints. Cross-validation procedures were used for all model performance evaluations to reduce bias in sample selections. Results showed that unconstrained ML models produced predictions that violate monotonicity in many parts of the input space. However, by adding monotonicity constraints into ANN models, monotonicity violations were effectively reduced while maintaining relatively high performance, thus providing a more robust and interpretable prediction. Using slope stability prediction as a proxy, the methods developed in this study to incorporate monotonicity constraints into ML models can be applied to many geotechnical engineering applications. The proposed approach enhances the reliability and interpretability of ML models, resulting in more accurate and consistent outcomes for real-world applications.

As renewable energy penetration in power grids increases, inherent intermittency and volatility pose severe stability challenges. Traditional assessment methods face computational complexity bottlenecks, while machine learning (ML) models, despite strong predictive performance, suffer from “black-box” opacity limiting adoption in safety-critical systems. This study proposes a comprehensive ML framework integrating LIME interpretability and physical mechanism validation for renewable energy grid stability assessment, using a publicly available synthetic benchmark dataset (2000 samples, 15 features) adhering to IEC/IEEE standards. We systematically compare ten classification models spanning traditional ML (logistic regression, K-nearest neighbors, support vector machine, random forest, gradient boosting) and deep learning (MLP, 1D CNN, LSTM, GRU, tabular ResNet). Results demonstrate traditional methods’ competitive performance on this small-scale tabular dataset—gradient boosting achieves 84.5% accuracy, ROC AUC 0.904, MCC 0.626, outperforming deep architectures constrained by overfitting on 1600 training samples. We emphasize that these findings are specific to the limited dataset scale ( ); deep learning may demonstrate superior performance with substantially larger training sets ( ). We develop a four-level LIME interpretability framework: (i) single-sample local interpretation, (ii) global feature importance aggregation identifying FreqDeviation and HarmonicTHD as most critical factors (contributions > 10), (iii) contribution directionality analysis, and (iv) multi-sample consistency verification (CV < 15%). Rationale for CV < 15%: This threshold balances coverage (88% of samples) with reliability, filtering unstable interpretations while retaining robust feature rankings, validated through empirical testing and expert assessment. LIME-identified features align with power system theory—frequency deviation reflects active power imbalance, harmonic distortion affects damping. However, we identify spurious correlations (Sample 127: low harmonics paradoxically promoting instability), underscoring expert validation necessity before deployment. Exploratory LLM integration (GPT-4) demonstrates feasibility of converting LIME outputs into actionable natural language recommendations, achieving expert-rated technical accuracy 4.2 ± 0.6/5. We explicitly acknowledge critical limitations: (i) synthetic data may not capture all real-world complexities (rare failure modes, cascading failures, stochastic weather patterns), requiring validation on actual SCADA/PMU data; (ii) the evaluated deep learning architectures exclude recent state-of-the-art tabular models (e.g., FT-transformer, TabNet), limiting the scope of our deep learning assessment; (iii) model performance may degrade by 5–15% on real data without domain adaptation. This framework advances explainable AI for power systems, providing pragmatic guidance for practitioners and establishing rigorous interpretability methodology transferable to physics-constrained ML tasks.

Susceptibility assessment is a crucial task for mitigating landslide hazards. It includes displacement prediction, stability analysis, and location prediction for individual hillslopes or regional mountainous areas. Physically based models can assess landslide susceptibility with limited datasets by inputting physical parameters, albeit with some uncertainties. In contrast, data-driven models, primarily developed using machine learning and statistical algorithms, often provide acceptable predictive accuracy in assessing landslide susceptibility. They generally serve as practical tools for prediction but lack transparency and scientific interpretability. This review critically analyzes the strengths, limitations, and application scenarios of each model type, with a focus on recent advancements, practical applications, and challenges encountered. Furthermore, potential integration strategies are discussed to address the limitations of each approach, including hybrid models that combine the interpretability of physically based models with the predictive power of data-driven models. Finally, we suggest future research directions to improve landslide susceptibility assessments, such as enhancing model interpretability, incorporating real-time monitoring data, enhancing cross-regional transferability, and leveraging advancements in remote sensing, spatial data analytics, and multi-source data fusion.

Background Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result. Results To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival. Conclusion PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

The growing environmental challenges associated with plastic waste disposal and the need for sustainable pavement construction practices have prompted significant research interest in incorporating recycled plastics into asphalt mixtures. However, accurately predicting the performance characteristics of plastic-modified asphalt mixtures, particularly Marshall Stability (MS) and Marshall Flow (MF), remains a critical yet challenging task due to complex nonlinear relationships between mixture constituents. This study addresses this issue by developing reliable predictive models using machine learning techniques including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), further optimized through Particle Swarm Optimization (PSO). A comprehensive dataset comprising 210 samples of plastic-modified asphalt mixtures was utilized, incorporating inputs such as plastic content and size, bitumen content, maximum aggregate size, mixing temperature, and compaction effort (number of blows), to predict MS and MF as outputs. Results showed that the PSO-optimized XGB model achieved the highest accuracy, yielding R 2 values of 0.82 for MS and 0.83 for MF. Model interpretability was enhanced using advanced techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, and Taylor diagrams, quantitatively highlighting optimal plastic particle sizes (2.5–4 mm), bitumen content (5.3–5.5%) and plastic content (20–30%). These findings provide actionable insights that support safer and longer-lasting pavements, promote the sustainable reuse of waste plastics, and enable cost-effective mix design strategies for modern asphalt construction.