To address the large amount of calculation and low efficiency during each iteration in large-scale data scenarios when using active-set method to solve the problem of Support Vector Data Description （SVDD）， an efficient Active-Set Method for SVDD problem with Gaussian kernel （ASM-SVDD） was designed. Firstly， due to the peculiarity of constraint conditions in SVDD dual model， a dimension-reduced subproblem with equality constraints was solved in each iteration. Then， the active-set was updated through matrix manipulations. Each update calculation was only related to the existing support vectors and a single sample point， which reduced the amount of computation dramatically. In addition， since ASM-SVDD algorithm can be seen as a variant of the traditional active-set method， the limited termination of this algorithm was obtained by applying the theory of active-set method. Finally， simulation and real datasets were used to verify the performance of ASM-SVDD algorithm. The results show that ASM-SVDD algorithm can improve the model performance effectively as the number of training rounds increases. Compared to the fast incremental algorithm to solve SVDD problem — FISVDD （Fast Incremental SVDD）， ASM-SVDD algorithm has the objective value obtained by training reduced by 25.9% and the recognition ability of support vectors improved by 10.0% on the typical low-dimensional high-sample dataset shuttle. At the same time， ASM-SVDD algorithm obtains F1 scores on different datasets all higher than FISVDD algorithm with the maximum improvement of 0.07% on the super large-scale dataset criteo. It can be seen that ASM-SVDD algorithm can obtain more stable hypersphere through training， and obtain higher judgment accuracy of test samples while performing outlier detection. Therefore， ASM-SVDD algorithm is suitable for outlier detection in large-scale data scenarios.

To improve the accuracy of watershed runoff volume prediction， and considering the lack of model transparency and physical interpretability of data-driven hydrological model， a new runoff forecast model named Graph Attention neTwork and Dual-stage Attention mechanism-based Long Short-Term Memory network （GAT-DALSTM） was proposed. Firstly， based on the hydrological data of watershed stations， graph neural network was introduced to extract the topology of watershed stations and generate the feature vectors. Secondly， according to the characteristics of hydrological time series data， a runoff forecast model based on dual-stage attention mechanism was established to predict the watershed runoff volume， and the reliability and transparency of the proposed model were verified by the model evaluation method based on attention coefficient heat map. On the Tunxi watershed dataset， the proposed model was compared with Graph Convolution Neural network （GCN） and Long Short-Term Memory network （LSTM） under each prediction step. Experimental results show that， the Nash-Sutcliffe efficiency coefficient of the proposed model is increased by 3.7% and 4.9% on average respectively， which verifies the accuracy of GAT-DALSTM runoff forecast model. By analyzing the heat map of attention coefficient from the perspectives of hydrology and application， the reliability and practicability of the proposed model were verified. The proposed model can provide technical support for improving the prediction accuracy and model transparency of watershed runoff volume.

The existing image super-resolution reconstruction methods are affected by texture distortion and details blurring of generated images. To address these problems， a new image super-resolution reconstruction network based on multi-channel attention mechanism was proposed. Firstly， in the texture extraction module of the proposed network， a multi-channel attention mechanism was designed to realize the cross-channel information interaction by combining one-dimensional convolution， thereby achieving the purpose of paying attention to important feature information. Then， in the texture recovery module of the proposed network， the dense residual blocks were introduced to recover part of high-frequency texture details as many as possible to improve the performance of model and generate high-quality reconstructed images. The proposed network is able to improve visual effects of reconstructed images effectively. Besides， the results on benchmark dataset CUFED5 show that the proposed network has achieved the 1.76 dB and 0.062 higher in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） compared with the classic Super-Resolution using Convolutional Neural Network （SRCNN） method. Experimental results show that the proposed network can increase the accuracy of texture migration， and effectively improve the quality of generated images.