Yang Liu

In response to the limited capability of extracting semantic information in knowledge graph completion methods, we propose a model that combines spatial transformation and attention mechanisms (STAM) for knowledge graph embedding. Firstly, spatial transformation is applied to reorganize entity embeddings and relation embeddings, enabling increased interaction between entities and relations while preserving shallow information. Next, a two-dimensional convolutional neural network is utilized to extract complex latent information among entity relations. Simultaneously, a multi-scale channel attention mechanism is constructed to enhance the capture of local detailed features and global semantic features. Finally, the surface-level shallow information and latent information are fused to obtain feature embeddings with richer semantic expression. The link prediction results on the public datasets WN18RR, FB15K237 and Kinship demonstrate that STAM achieved improvements of 8.8%, 10.5% and 6.9% in the mean reciprocal rank (MRR) evaluation metric compared to ConvE, for the respective datasets. Furthermore, in the link prediction experiments on the hydraulic engineering dataset, STAM achieves better experimental results in terms of MRR, Hits@1, Hits@3 and Hits@10 evaluation metrics, demonstrating the effectiveness of the model in the task of hydraulic engineering knowledge graph completion.

To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (E _ns ) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

The strong randomness exhibited by runoff series means the accuracy of flood forecasting still needs to be improved. Mode mixing can be dealt with using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the endpoint effect of CEEMDAN can be successfully dealt with using the mutual information criterion. To increase the computational effectiveness of broad learning (BL), orthogonal triangular matrix decomposition (QR) was used. A novel improved coupled CEEMDAN-QRBL flood forecasting model was created and applied to the prediction of daily runoff in Xiaolangdi Reservoir based on the benefit of quick calculation by the model output layer. The findings indicate that the enhanced QRBL is 28.92% more computationally efficient than the BL model, and that the reconstruction error of CEEMDAN has been decreased by 48.22%. The MAE of the improved CEEMDAN-QRBL model is reduced by 12.36% and 16.31%, and the Ens is improved by 8.81% and 3.96%, respectively, when compared with the EMD-LSTM and CEEMDAN-GRU models. The predicted values of the CEEMDAN-QRBL model have a suitable fluctuation range thanks to the use of nonparametric kernel density estimation (NPKDE), which might serve as a useful benchmark for the distribution of regional water resources.

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect. Finally, VMD and OS-QR-ELM were combined to construct a drought prediction method based on the VMD-OS-QR-ELM. The reliability and accuracy of the VMD-OS-QR-ELM model were improved by 86.19% and 93.20%, respectively, compared with those of the support vector regression model combined with empirical mode decomposition. Furthermore, the calculation efficiency of the OS-QR-ELM model was increased by 88.65% and 85.32% compared with that of the ELM and QR-ELM models, respectively.

To solve the problems of low prediction accuracy, poor stability, and low calculation efficiency in runoff forecasting, this study develops an extreme learning machine (ELM) model based on improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The model first uses orthogonal triangular decomposition (QR) to redefine the ELM hidden layer output to construct the orthogonal triangular ELM (QR-ELM). It then introduces the online sequence mechanism (OS) into the QR-ELM model to construct online sequence OR-ELM (OS-QR-ELM), which can effectively improve the efficiency of the ELM models. Then, it uses the correlation wave extension method to extend both ends of the original signal to solve the end effect of CEEMDAN. Finally, it integrates CEEMDAN and OS-QR-ELM combined with parallel computing ideas to construct a parallel CEEMDAN-OS-QR-ELM runoff prediction method. The experimental results show that compared with the support vector regression method combined with empirical mode decomposition, the accuracy and reliability of the parallel CEEMDAN-OS-QR-ELM model was 1.09% and 8% higher, respectively. Compared with the ELM model, the efficiency of OS-QR-ELM was 5%.

In this study, a new broad learning (BL) model based on an improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is proposed to resolve the low accuracy, poor robustness, and long delay problems that are present in current drought assessments. First, the extreme delay method was applied to improve the CEEMDAN end effect. The improved CEEMDAN method was then used to decompose a series of non-steady-state signals from drought monitoring into multiple steady-state components. A BL model based on orthogonal trigonometry (QR) was then used to predict these multiple steady-state components, and the predicted components were further reorganised to obtain a high-precision drought sequence. On this basis, CEEMDAN was introduced into the orthogonal triangular broad learning (QR-BL), and a drought prediction model (CEEMDAN-QR-BL) combining CEEMDAN and QR-BL was proposed. Finally, the De Martonne aridity index was used to calculate the drought sequence results and determine the drought grades. To meet the real-time requirements of drought prediction, parallel computing was introduced into the CEEMDAN-QR-BL model, and a drought prediction method based on parallel CEEMDAN-QR-BL was constructed. The experimental results show that, when compared with a support vector regression model combined with an empirical mode decomposition, the reliability and accuracy of the CEEMDAN-QR-BL increases by 29.57% and 11.84%, respectively. In addition, when compared with only BL, the prediction efficiency of QR-BL improved by 62.29%.