Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province.

Understanding the distribution and drivers of PM concentrations in the Yangtze River Delta from 2015 to 2020 using Random Forest Regression.

Understanding the drivers of PM is critical for the establishment of PM prediction models and the prevention and control of regional air pollution. In this study, the Yangtze River Delta is taken as the research object. Spatial cluster and outlier method was used to analyze the temporal and spatial distribution and variation of surface PM in the Yangtze River Delta from 2015 to 2020, and Random Forest was utilized to analyze the drivers of PM in this area.

[Seasonal variation and driving factors of forest fire in Zhejiang Province, China, based on MODIS satellite hot spots].

Understanding the changes and driving factors of forest fire can provide scientific basis for prevention and management of forest fire. In this study, we analyzed the changes and driving factors of forest fire in Zhejiang Province during 2001-2016 based on trend analysis and Logistic regression model with the MODIS satellite fire point data combined with meteorological (daily ave-rage wind speed, daily average temperature, daily relative humidity, daily temperature difference, daily cumulative precipitation), human activities (distance from road, distance from railway, distance from resident, population, per capita GDP), topographic and vegetation factors (elevation, slope, vegetation coverage). The results showed that the number of forest fires in spring and summer had significantly increased, while the forest fires in the autumn and winter increased first and then decreased.

Understanding fire drivers and relative impacts in different Chinese forest ecosystems.

In this study, spatial patterns and driving factors of fires were identified from 2000 to 2010 using Ripley's K (d) function and logistic regression (LR) model in two different forest ecosystems of China: the boreal forest (Daxing'an Mountains) and sub-tropical forest (Fujian province). Relative effects of each driving factor on fire occurrence were identified based on standardized coefficients in the LR model. Results revealed that fires were spatially clustered and that fire drivers vary amongst differing forest ecosystems in China.

[Prediction model of human-caused fire occurrence in the boreal forest of northern China].

The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g.