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.

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.