The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity (n,%), Schmidt rebound number (R_n), longitudinal wave velocity (V_p, m/s), and point load strength (I_s(50), MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a prediction model of uniaxial compressive strength was built based on the SSA-XGBoost model. To verify the effectiveness of the SSA-XGBoost model, empirical formulas, XGBoost, SVM, RF, BPNN, KNN, PLSR, and other models were also established and compared with the SSA-XGBoost model. All models were evaluated using the root mean square error (RMSE), correlation coefficient (R²), mean absolute error (MAE), and variance interpretation (VAF). The results calculated by the SSA-XGBoost model (R² = 0.84, RMSE = 19.85, MAE = 14.79, and VAF = 81.36), are the best among all prediction models. Therefore, the SSA-XGBoost model is the best model to predict the uniaxial compressive strength of rock, for the dataset tested.

Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent optimisation algorithms and deep learning techniques and collects 287 sets of real rockburst data to form a sample database, in which six quantitative indicators are selected as feature parameters. In order to validate the effectiveness of the constructed eight machine learning prediction models, the study selected Accuracy, Precision, Recall and F1 Score to evaluate the prediction performance of each model. The results show that the NGO-CNN-BiGRU-Attention model has the best prediction performance, with an accuracy of 0.98. Subsequently, engineering validation of the model is carried out using eight sets of real rockburst data from Daxiangling Tunnel, and the results show that the model has a strong generalisation ability and can satisfy the relevant engineering applications. In addition, this paper also uses SHAP technology to quantify the impact of different factors on the rockburst intensity level and found that the elastic strain energy index and stress ratio have the greatest impact on the rockburst intensity level.

Nitrous oxide produced during wastewater treatment is a major greenhouse gas, and accurate prediction and control of N₂O emissions are crucial for achieving carbon neutrality. In this study, aiming to address the complex issues of N₂O emission prediction in wastewater treatment, large-scale multidimensional data from the Altenrhein wastewater treatment plant was used to build a sample database. The role of symmetry in model architecture and data analysis was discussed, and six intelligent prediction models for N₂O emissions were proposed based on deep learning technology. The results showed that the PLO-CNN-BiLSTM-Attention model achieved the best performance, with an R² of 0.99 on the test set. Engineering validation using 48 subsequent datasets confirmed the model’s strong generalization ability and robustness. Feature importance analysis based on SHAP revealed that water temperature was the most critical factor influencing N₂O emissions, while dissolved oxygen concentration and inlet flow rate also had impacts but showed a certain symmetrical change between summer and winter. This study provides efficient and reliable technical support for monitoring and predicting N₂O emissions in urban wastewater treatment plants and offers a scientific basis for developing strategies to reduce greenhouse gas emissions.

Landslide dams, formed by natural disasters or human activities, pose significant challenges for lifespan prediction, which is crucial for effective water conservancy management and disaster prevention. This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. The model integrates CNN’s local feature extraction with Transformer’s global modeling capabilities, effectively capturing the nonlinear dynamics of key parameters affecting landslide dam lifespan. The IBKA ensures optimal parameter tuning, which enhances the model’s adaptability and generalization, especially when dealing with small-sample datasets. Experiments utilizing multi-source heterogeneous datasets compare the proposed model with traditional machine learning and deep-learning approaches, including LightGBM, MLP, SVR, CNN–Transformer, and BKA–CNN–Transformer. The results show that the IBKA–CNN–Transformer achieves R² values of 0.99 on training data and 0.98 on testing data, surpassing the baseline methods. Moreover, SHapley Additive exPlanations analysis quantifies the influence of critical features such as dam length, reservoir capacity, and upstream catchment area on lifespan prediction, improving model interpretability. This approach not only provides scientific insights for risk assessment and decision making in landslide dam management but also demonstrates the potential of deep learning and optimization algorithms in broader geological disaster management applications.

Uniaxial compressive strength (UCS) is a fundamental parameter for rock engineering design and stability assessment, but direct laboratory testing is costly, time-consuming, and often difficult for weak or fractured rocks. To improve predictive accuracy while preserving mechanical interpretability, this study proposes a Gated Empirical-Law LightGBM model (GEL-LightGBM). The framework embeds three representative rock-strength priors, including point-load strength, multi-index strength, and porosity-degradation relationships, as empirical-law experts. A sample-adaptive gating mechanism dynamically assigns its contributions for different rock states, while a controlled residual corrector captures nonlinear deviations between empirical estimates and measured UCS. Using 344 published rock-mechanics samples, porosity, Schmidt rebound hardness, P-wave velocity, and point-load strength index were used as predictors. GEL-LightGBM outperformed LightGBM, XGBoost, random forest, MLP, CNN, SVR, and BPNN, achieving a testing R2 of 0.9790 and an RMSE of 7.5623 MPa. SHAP analysis identified porosity as the dominant factor, contributing 49.0%, followed by rebound hardness (32.2%) and P-wave velocity (17.2%). The strongest interaction occurred between porosity and rebound hardness (2.31 MPa). These findings indicate that GEL-LightGBM provides accurate, stable, and physically interpretable UCS prediction for heterogeneous rock datasets.