Black and odorous water bodies represent a topic of significant interest in the field of water pollution prevention and control. Remote sensing technology is increasingly exploited for the monitoring of black and odorous water bodies because of its high efficiency and large-scale monitoring potential. In the present study, the Sentinel-2A imagery data were combined with data obtained by measuring spectral properties of black and odorous water bodies to produce a classification and regression tree (CART) model-based improved remote sensing recognition method for such water bodies. This method transforms the traditional single-feature empirical threshold segmentation algorithm to a multi-feature fuzzy decision-tree classification algorithm. The results reveal overall accuracy values of 84.78%, 92.85%, and 72.23% for the CART decision-tree algorithm, the confidence zone classification, and the fuzzy zone node classification, respectively. The method proposed in the present study enables the highly precise extraction of features representing black and odorous water bodies from satellite imagery. The characterization of confidence and fuzzy zones minimizes the need for field inspections, and it enhances the efficiency of diverse applications including engineering.

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product.

Soil moisture is one of the most critical elements of the Earth system and is essential for the study of the terrestrial water cycle, ecological processes, climate change, and disaster warnings. In this study, the training sample was selected to divide the dataset according to months from 2000 to 2018 after the advantages of three training samples were compared using a backpropagation (BP) neural network model. Furthermore, the monthly surface soil moisture in China in 2019 and 2020 was simulated based on various meteorological elements. The results demonstrate that evapotranspiration has the greatest influence on soil moisture among the various meteorological factors, followed by precipitation on a national scale throughout the year. Additionally, the accuracy of the training and simulation results with BP neural networks in the national winter months is slightly worse. In the future, the training samples of the BP neural network can be optimized following the differences in the dominant influence of various meteorological factors on soil moisture in different areas at different times to improve the simulation prediction accuracy.

In order to locate short circuit fault in power cable systems accurately and in a timely manner, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. There are two main steps of the method: a) record the traveling-wave data and build a sheath current matrix; b) cluster the data and identify the arrival time by the characteristic cluster. The improvement mainly lies in that the high-dimensional data can be directly used for clustering. A PSCAD (Power System Computer Aided Design) simulation has been carried out for a selected HV (high voltage) cable circuit. Results indicate that the hierarchical structure of condensed cluster tree just corresponds to the positional relationship between the fault point and monitoring points. The clustering results can be used for the identification of the arrival time of the traveling wave. The potential problems of the method lie in the “noise” identification. Ordinary samples can be labeled as “noise” by HDBSCAN. The combined use of DBSCAN and HDBSCAN maybe a solution for the “noise” problem.