Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.
The development of knowledge graph needs the support of a vast quantity of data. However, the amount of data increases rapidly is placing increasing demands on machines. Centralized data storage requires high-performance hosts to store data, which is costly and have single point of failure. Distributed data storage can reduce the cost of the machine greatly, and there is no single point of failure, but it has requirements for partition and storage of data collection. In the knowledge storage of specific domain, the way of graph data partition and storage vary from the different domain knowledge. To solve the above problems, a scheme of graph partition and distributed storage for domain-specific knowledge graphs is proposed. The proposed graph partition scheme pays attention to the correlation between the data, and divides the nodes affiliated each other into the same or similar partition. A distributed aggregation storage scheme is designed, which makes full use of cluster performance and solves the problem of data consistency during data insertion and update. The proposed distributed storage scheme based on HBase combines Neo4j to realize visual query effectively. Experimental results show the efficiency and the effectiveness of the proposed method in partition time, the number of edge-cut and update time.
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