Urban flood susceptibility has emerged as a critical challenge for cities worldwide, exacerbated by rapid urbanization. This study evaluates urban flood susceptibility under different Shared Socioeconomic Pathways (SSPs) in the context of urbanization. A coupled modeling approach integrating the System Dynamics (SD) model and the Future Land Use Simulation (FLUS) model was employed to project future land use changes under sustainable development, moderate development, and conventional development scenarios. Additionally, an XGBoost model was developed to assess urban flood susceptibility. The results indicate that urban construction land will continue to increase over the next 30 years, with the extent of growth varying across different scenarios. Notably, under the conventional development scenario, rapid economic growth leads to a significant expansion of built-up land and a sharp decline in ecological land, which in turn exacerbates the urban flood susceptibility. Consequently, urban flood susceptibility is projected to increase across all three scenarios, albeit at varying rates. Specifically, under the sustainable development scenario, 27% of Guangzhou is projected to face high flood risk. In the moderate development scenario, the area classified as high-risk increased by 868.73 km². Under the conventional development scenario, the high-risk area expanded from 1282.9 km² in 2020 to 2761.33 km², representing a 16% increase. These differences are primarily attributed to changes in land use, which alter surface runoff and subsequently enhance the city’s vulnerability to waterlogging. This study provides a comprehensive framework for assessing urban flood susceptibility in the context of urbanization, offering valuable insights for formulating targeted flood prevention and mitigation strategies.

Improving the accuracy of power load forecasting is an important link in the optimization of power systems. Most of the existing studies in the short-term load forecasting task at present have the problem of insufficient extraction of multi-scale features. Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. First, multi-feature power load data were collected. Second, the random forest algorithm was used to preprocess the data. Next, multi-scale GCN was utilized to model the multi-scale spatio-temporal features in the power load data. The data processed by the multi-scale GCN were input into the improved Transformer module based on MLLA to extract long-term temporal dependencies. Subsequently, comparative experiments and ablation experiments were conducted on three public power datasets. The experimental results show that, compared to the comparative model, for the ETTh1 dataset, the RMSE index of this model decreased by up to 0.314, the MAE decreased by up to 0.304, and the R² index result improved by up to 9.45%. For the ETTm1 dataset, the RMSE index of this model decreased by up to 0.266, the MAE decreased by up to 0.231, and the R² index result improved by up to 3.3%. For the Australian dataset, the RMSE index of this model decreased by up to 494.366, the MAE decreased by up to 493.127, and the R² index result improved by up to 54%, verifying the superiority and effectiveness of the proposed model.

To enhance exploitation for artificial bee colony (ABC) algorithm, we propose a modified multi-strategy ABC variant in which a superior information learning strategy is employed. In this strategy, the individuals can learn superior information from an exemplar which has better fitness value, and the exemplar is no longer acted by the global best individual or the elite group. In experiments, 22 well-known test functions are used and six well-established ABC variants are involved in the comparison. The results show that our approach performs better on most of test functions. Furthermore, our approach is applied to solve the node coverage optimisation problem in wireless sensor network. To this end, an improved Boolean sensing model is used to model the objective function, and simulation results indicate that our approach can provide promising performance.