随机森林算法在全球干旱评估中的应用

干旱是发生频率最高,造成社会、经济损失和生态破坏最严重、最广泛的自然灾害之一,因此对干旱进行可靠、有效的评估十分重要.本文以月平均降水、月平均温度、月最高温度、月最低温度、土壤湿度、蒸散发、NDVI、叶绿素荧光等作为解释变量,以基于SPI的干旱等级作为目标变量,采用随机森林算法,以2007-2012年的数据作为训练数据...

Application of Random Forest Algorithm in Global Drought Assessment

Drought is one of the most frequent and widespread climate extremes, causing devasting social, economic and ecological damages. It is of key importance to evaluate drought reliably and effectively. In this study, in order to assess global drought grade, the Random Forest (RF) algorithm was used to establish the drought grade assessment models for the 11 climate zones in the world. We chose monthly mean precipitation, mean temperature, maximum temperature, minimum temperature, soil moisture, evapotranspiration (ET), Normalized Difference Vegetation Index (NDVI), and Sun/Solar-induced Chlorophyll Fluorescence (SIF) as explanatory variables and drought grades based on Standardized Precipitation Index (SPI) as target variable. The SPI on different timescales of 1 month, 3 months, 6 months and 12 months were labeled as SPI1, SPI3, SPI6 and SPI12, respectively. The data from 2007 to 2012 were used as training data of the assessment models while those from 2013 to 2014 were used as prediction data. The results showed that: (1) The temporal scale of SPI influenced the model accuracy. Among the models with drought grade based on SPI1、SPI3、SPI6 and SPI12, the one with drought grade based on SPI1 had the highest accuracy (60%~75%) and prediction performance. The model with drought grade based on SPI1 was able to capture 90.91% of the drought records in the global emergency events database (EM-DAT). It could capture 78.47% of the drought duration month in the EM-DAT. The agreements with records and drought duration month in the EM-DAT indicated the good performance of the drought grade assessment model based on 1-month SPI and RF algorithm. (2) The drought grading criterion had little impact on the model performance. Users could select criterion I (drought/not drought) or criterion II (severe/not severe) depending on the real needs. (3) The relative importance of each explanatory variable depended on both the temporal scale of SPI and climatic differences. Precipitation was the most important factor for the drought grade based on SPI1. The importance of precipitation decreased and the ones of other explanatory variables such as temperature, soil moisture, NDVI, and ET increased as the timescale of SPI increased. The importance of variables except precipitation showed differences in different climate zones. Among the tropical, subfrigid, and tundra climate zones, temperature or ET is relatively important for drought. Soil moisture is relatively important in dry climate zone and precipitation is the most important in mild temperate climate zone, while vegetation is relatively important in the humid continental climate zone.