In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The BPNN model is most sensitive to the data characteristics consistency.

Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly runoff, a novel hybrid data-driven model using particle swarm optimization coupled with flower pollination algorithm-based deep belief networks (PSO-FPA-DBNs) was proposed, which selected the optimal network depth via PSO and searched for the optimum hyper parameters (the number of neurons in the hidden layer and the learning rate of the RBMs) in the DBN using FPA. The methodology was divided into three steps: (i) the Comprehensive Basin Response (COM) was constructed and calculated to characterize the hydrological state of the basin, (ii) the information entropy algorithm was adopted to select the key factors, and (iii) the novel model was proposed for monthly runoff forecasting. We systematically compared the PSO-FPA-DBN model with the traditional prediction models (i.e., the backpropagation neural network (BPNN), support vector machines (SVM), deep belief networks (DBN)), and other improved models (DBN-PLSR, PSO-GA-DBN, and PSO-ACO-DBN) for monthly runoff forecasting by using an original dataset. Experimental results demonstrated that our PSO-FPA-DBN model outperformed the peer models, with a mean absolute percentage error (MAPE) of 18.23%, root mean squared error (RMSE) of 230.45 m³/s, coefficient of determination (DC) of 0.9389, and qualified rate (QR) of 64.2% for the data from the Yalong River Basin. Also, the stability of our PSO-FPA-DBN model was evaluated. The proposed model might adapt effectively to the nonlinear characteristics of monthly runoff forecasting; therefore, it could obtain accurate and reliable runoff forecasting results.

Underwater creatures play a vital role in maintaining the delicate balance of the ocean ecosystem. In recent years, machine learning methods have been developed to identify underwater biologicals in the complex underwater environment. However, the scarcity and poor quality of underwater biological images present significant challenges to the recognition of underwater biological targets, especially multi-target recognition. To solve these problems, this paper proposed an ensemble method for underwater biological multi-target recognition. First, the CutMix method was improved for underwater biological image augmentation. Second, the white balance, multiscale retinal, and dark channel prior algorithms were combined to enhance the underwater biological image quality, which could largely improve the performance of underwater biological target recognition. Finally, an improved model was proposed for underwater biological multi-target recognition by using a mask region-based convolutional neural network (Mask-RCNN), which was optimized by the soft non-maximum suppression and attention-guided context feature pyramid network algorithms. We achieved 4.97 FPS, the mAP was 0.828, and the proposed methods could adapt well to underwater biological multi-target recognition. The recognition effectiveness of the proposed method was verified on the URPC2018 dataset by comparing it with current state-of-the-art recognition methods including you-only-look-once version 5 (YOLOv5) and the original Mask-RCNN model, where the mAP of the YOLOv5 model was lower. Compared with the original Mask-RCNN model, the mAP of the improved model increased by 3.2% to 82.8% when the FPS was reduced by only 0.38.

Accurate runoff simulation is essential for water resources planning and development projects. At present, the commonly employed runoff simulation approaches are categorized into two types: process- and data-driven models. Process-driven models pertain to the enhancement of the structural framework in conceptual rainfall–runoff models using hydrological principles to estimate runoff but have low accuracy at the monthly scale. Unlike the process-driven models, data-driven models (DDMs) can simulate the relationship between input factors and output runoff data without regard to complex and unknown runoff production and acquire satisfactory simulation results. Here, we comparatively investigate the applicability of DDMs, including traditional shallow DDMs, deep learning-based (DL) models for monthly runoff simulation, and select the Autoregressive (AR) model as the baseline model for comparison. Moreover, four evaluation indicators, including mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R²), are employed to evaluate the runoff simulation effects of the above methods. We systematically compare the AR model, and shallow and deep learning-based DDMs for runoff simulation at four hydrological stations in the Yalong River basin (YRB), respectively. The finding results reveal that the DDMs demand limited data and can offer satisfactory prediction effects. Also, the DL models outperform other shallow DDMs and the AR model in terms of the above evaluation criteria.

Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods.