Automated water body detection from satellite imagery is a fundamental stage for urban hydrological studies. In recent years, various deep convolutional neural network (DCNN)-based methods have been proposed to segment remote sensing data collected by conventional RGB or multispectral imagery for such studies. However, how to effectively explore the wider spectrum bands of multispectral sensors to achieve significantly better performance compared to the use of only RGB bands has been left underexplored. In this article, we propose a novel DCNN model-multichannel water body detection network (MC-WBDN)-that incorporates three innovative components, i.e., a multichannel fusion module, an Enhanced Atrous Spatial Pyramid Pooling module, and Space-to-Depth/Depth-to-Space operations, to outperform state-of-the-art DCNN-based water body detection methods. Experimental results convincingly show that our MC-WBDN model achieves remarkable water body detection performance, is more robust to light and weather variations, and can better distinguish tiny water bodies compared to other DCNN models.

The rapid spread of mobile banking and e-commerce has coincided with a dramatic increase in fraudulent online payments in recent years. Although machine learning and deep learning are widely used in credit card fraud detection, the typical credit card transaction data set is unbalanced, and the fraud data is much less than the normal transaction data, limiting the effectiveness of traditional binary classification algorithms. To overcome this issue, researchers oversample minority class data and utilize ensemble learning classification algorithms. However, oversampling still has disadvantages. Hence, we improve the generator part of the Variational Autoencoder Generative Adversarial Network (VAEGAN) and propose a new oversampling method that generates convincing and diverse minority class data. The training set is enhanced by generating minority class fraud data to train the ensemble learning classification model. The method is tested on an open credit card dataset, with the experimental results demonstrating that the oversampling method utilizing the improved VAEGAN is superior to the oversampling method of Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Synthetic Minority Oversampling Technique (SMOTE) in terms of Precision, F1_score, and other indicators. The oversampling method based on the improved VAEGAN effectively deals with the classification problem of imbalanced data.

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can fly a safe path with high accuracy under static or uncertain dynamic obstacles environments.

The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we propose a satellite network traffic forecasting method with an improved gate recurrent unit (GRU). This method combines the attention mechanism with GRU neural network, fully mines the characteristics of self-similarity and long correlation among traffic data sequences, pays attention to the importance of traffic data and hidden state, learns the time-dependent characteristics of input sequences, and mines the interdependent characteristics of data sequences to improve the prediction accuracy. Particle Swarm Optimization (PSO) algorithm is used to obtain the best network model Hyperparameter and improve the prediction efficiency. Simulation results show that the proposed method has the best fitting effect with real traffic data, and the errors are reduced by 26.9%, 37.2%, and 57.8% compared with the GRU, Support Vector Machine (SVM), and Fractional Autoregressive Integration Moving Average (FARIMA) models, respectively.

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.