In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L 1 L 2 -based variational retinex (AD-L 1 L 2 VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L 1 L 2 -based variational retinex (L 1 L 2 VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods.

Understanding the spatial and temporal distribution patterns of fire is of ecological, social and economic importance. The purpose of this study is to examine the spatial distribution of high fire risk using machine learning algorithms and early warning weather in high-risk areas. Take the satellite monitored fire point data in Yunnan Province during 2015–2019 as an example. The spatial distribution law of high-density parts is found using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, and the correlation degree analysis of high-density fire areas based on clustering and annual mean meteorological factors is carried out using grey correlation analysis (GCA) method. Results illustrates that within five years, fires frequently occurred in the seven regions of Wenshan Zhuang and Miao Autonomous Prefecture, Honghe Hani and Yi Autonomous Prefecture, Lijiang City, Pu’er City, and Xishuangbanna Dai Autonomous Prefecture. Among them, the fire in Lijiang had the greatest relationship with precipitation, Pu’er and Xishuangbanna had the greatest correlation with temperature, and Honghe Hani and Wenshan Zhuang were most affected by wind speed. This article acclaims fire prevention in key periods, key areas, key weather and reinforces the protection of transmission lines in the risk area.

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

In recent years, the frequent fouling accidents have posed a serious threat to people’s life and property safety. Owing to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labor-intensive task to map the pollution distribution using traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. K-nearest neighbor, support vector machine) are used to analyze the effects of conditions such as multiple air pollution and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 72.32% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional map. Last, the remote sensing-based map and the manual measurement-based map were combined with corresponding experience weight to obtain a weighted pollution distribution map.

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