The increasing frequency of extreme weather events raises the likelihood of forest ***,establishing an effective fire prediction model is vital for protecting human life and property,and the *** study aims to build a ...
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The increasing frequency of extreme weather events raises the likelihood of forest ***,establishing an effective fire prediction model is vital for protecting human life and property,and the *** study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire *** monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the september to April fire season in fujian Province,*** compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(sGALM).The results indicate that sGALMs had the best fitting results and the highest prediction *** factorssignificantly impacted forest fires in fujian *** with high fire incidence were mainly concentrated in the northwest and *** improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear *** model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
The increasing frequency of extreme weather events raises the likelihood of forest wildfires. Therefore, establishing an effective fire prediction model is vital for protecting human life and property, and the environ...
The increasing frequency of extreme weather events raises the likelihood of forest wildfires. Therefore, establishing an effective fire prediction model is vital for protecting human life and property, and the environment. Thisstudy aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers. Using monthly grid data from 2006 to 2020, a modeling study analyzed fire occurrences during the september to April fire season in fujian Province, China. We compared the fitting performance of the logistic regression model(LRM), the generalized additive logistic model(GALM), and the spatial generalized additive logistic model(sGALM). The results indicate that sGALMs had the best fitting results and the highest prediction accuracy. Meteorological factorssignificantly impacted forest fires in fujian Province. Areas with high fire incidence were mainly concentrated in the northwest and southeast. sGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation. This model provides piecewise interpretations of forest wildfire occurrences, which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
North Gulf is China’s important sea route to Asia and Europe, and the seabed is rich in resources. For various reasons, the detection and mapping of submarine terrain are blank. The traditional multi-beam measurement...
North Gulf is China’s important sea route to Asia and Europe, and the seabed is rich in resources. For various reasons, the detection and mapping of submarine terrain are blank. The traditional multi-beam measurement method has high accuracy but uneven spatial distribution. Although the detection line process has high sampling and measurement accuracy, there is a large blank area between adjacent routes, which is difficult to achieve full-scale large area measurement due to environmental, time and budget constraints. On the other hand, the accuracy of the remote sensing inversion depth method is not high, but it can achieve a wide range of coverage, which is characterized by low cost, short cycle and convenient dynamic monitoring. This paper takes North Gulf as the research area, uses remote sensing data to construct the seabed topography, uses multi-beam data for checksum correction and uses distributed computing technology to distribute big data in different computing nodes, and the data are refined and reconstructed in three dimensions to form a 3D seabed topography. The method improves the efficiency and accuracy of multi-beam data processing and can construct a large area, high-resolution submarine DEM and realize database management.
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