Search

Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture and parameters using the particle swarm optimization (PSO) algorithm, a convolutional neural network (CNN) deep learning model was proposed to predict forest fire risk on a national scale. Utilizing fire data and fire risk factors from 2001 to 2020 in China, the PSO-CNN-based deep learning model (PSO-CNN) was utilized and tested. Compared to logistic regression, random forest, support vector machine, k-nearest neighbors, and CNN models, the PSO-CNN model exhibited superior performance with an accuracy of 82.2% and an AUC value of 0.92. These results clearly highlighted the effectiveness of the PSO-CNN model in enhancing the accuracy of forest fire prediction. Furthermore, the forest fire risk prediction level estimated by the proposed model on a national scale for the entire country was mostly consistent with actual fire data distribution, indicating its potential to be used as an important direction for deep learning in forest fire prediction research. [ABSTRACT FROM AUTHOR]

Sichuan is a high-incidence area of thunderstorm activity in China. Based on the data of the total lightning location system from 2018 to 2022, the total lightning, cloud-to-ground (CG) lightning, and intracloud (IC) lightning activity regularity for the Sichuan province (SC) and its three climate subregions: Sichuan Basin (SB), Panxi district (PD), and West Sichuan Plateau (WSP) are analyzed, and the influences of different climate and topography conditions on lightning activities are also discussed. The results show that (1) for the whole province, the annual mean value of total lightning is about 850 thousand. The SB has the most lightning occurrences, and the WSP has the largest IC and +CG proportion. The southwest of PD, the north-center of PD, and the southeast of SB are the three high-value centers of lightning density. (2) For SB, the better thermodynamic and moisture conditions account for the most lightning occurrences. For PD, the lightning distribution is attributed to the joint effect of specific meteorological conditions and mountainous topography. For WSP, the convections are weak and shallow, which lead to high IC and +CG proportion. (3) The IC lightning mainly occurs below 12 km, and the IC height is much lower on WSP. The spatial and seasonal variation of IC height corresponds well to the cloud base height (CBH) and cloud top height (CTH). (4) The seasonal lightning frequency distribution in the three regions is similar, but the diurnal variation is quite different. The lightning activity mainly occurs between 1400 and 2200 LT on WSP but shows obvious nocturnal in SB. (5) Most CG intensity concentrates in the range below 50 kA, and IC concentrates in the range below 75 kA. [ABSTRACT FROM AUTHOR]