疟疾预测的遗传规划方法与应用——以安徽省县(市)疟疾发病率为例  

Application of Genetic Programming on Predicting and Mapping Malaria in Anhui Province

作　　者：宋泳泽[1,2] 葛咏[2,3] 彭军还[1] 王劲峰[2] 任周鹏[2] 廖一兰[2] 

机构地区：[1]中国地质大学(北京)土地科学技术学院,北京100083 [2]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [3]江苏省地理信息资源开发与利用协同创新中心,南京210023

出　　处：《地球信息科学学报》2015年第8期954-962,共9页Journal of Geo-information Science

基　　金：国家重点基础研究发展计划项目("973"计划)(2012CB955503);国家科技支撑计划项目课题"贫困地区资源环境监测评估与生态价值评价技术"(2012BAH33B01);国家科技支撑计划项目课题"流动人口动态监测与信息获取关键技术研究"(2012BAI32B06)

摘　　要：疟疾是世界上最严重的一种寄生虫疾病,安徽省是典型的中纬度疟疾高发区域之一。本文以安徽省县级行政单元统计的疟疾发病率为例,从遥感监测数据中获取疟疾潜在驱动因素的数据,使用遗传规划方法建立遥感监测的环境因素与疟疾发病率之间的关系,从而预测疟疾发病率的空间分布,并分析预测结果、评价模型精度。结果表明,遗传规划方法预测的疟疾发病的精度(训练数据的预测R2=0.558,检验数据R2=0.429)较线性逐步回归方法的预测精度(训练数据的预测R2=0.470,检验数据R2=0.408)有所提高。遗传规划方法有利于提高预测疟疾发病率空间分布的精度。其为使用遥感监测数据预测疟疾的空间分布和变化的科学研究提供依据。This paper delineates the relationship between remote sensing monitoring indexes and malaria incidences using genetic programming（GP） method based on factors derived from remote sensing data. Thus, the spatial distribution of malaria incidence is predicted, the prediction results are analyzed, and the modeling precision is evaluated. Malaria is considered to be the severest parasite disease and Anhui Province is one of the typical mid-latitude areas coping with high malaria risk. This paper studies the issue of predicting malaria spatial distribution using GP method, as GP is a striking optimization method which has the capability of exploring a proper solution for sophisticated issues through evolutionary algorithms. And this process is further explained with an example adopting the monthly average malaria incidences in each county of Anhui Province from 2004 to 2010.Also, remote sensing data is regarded to be the main source of factors, considering its large spatial scale and fast data acquisition, and that various meteorological and environmental indexes, could be converted from remote sensing data. These factors include remote sensing indexes, such as normalized difference vegetation index（NDVI） and land surface temperature（LST）, plus natural attribute（elevation） and social attributes（population, immigrant and GDP data） in the county level. Results demonstrate that NDVI and LST have influences of two months＇ and one month＇s lag respectively. Compared with the result of linear regression（R2= 0.470 for training data and R2= 0.408 for test data）, the predicting precision is improved using GP method（R2= 0.558 for training data and R2= 0.429 for test data）, which is benefited from illustrating the non- linear relation between remote sensing indexes and malaria incidences. GP method contributes to increase the precision of predicting the spatial distribution of malaria incidence. Conclusively, this paper provides a basis for future scientific research on predicting spatial distributi

关 键 词：遗传规划 疟疾 遥感数据 空间分析 预测 

分 类 号：R531.3[医药卫生—内科学]