Prediction of Drought with Artificial Neural Networks and Genetic Algorithms Using Precipitation by Remote Perception

The aim of the study is to determine the performance of the regional agricultural drought prediction by the model of ANN(Artificial Neural Networks)type NARX,using the SPI(Standardized Precipitation Index),SPEI(Precipitation Index Standardized Evapotranspiration),VCI(Vegetation Condition Index)and GCI(Global Climate Indexes).There have been determined 10 homogeneous regions through RAF(regional frequency analysis)and L-moments,defining the most arid region and the index representing their respective time scale(SPEI 6 months)which responds to the growth and development of vegetation in the basin correlation Pearson equal to 0.58.Monthly rainfall and temperatures correspond to PISCO data prepared by SENAMHI-Peru,with space resolution of 0.05 degrees.For prediction,they have determined two groups,the first to build the model with 80% of the registration and validation of the model and the hypothesis with the remaining 20%.The results have been satisfactory prediction accepting the null hypothesis.

黄土高原植被变化对气象干旱多尺度响应特征与机制

基于黄土高原1983—2015年间的植被状况指数(VCI)与1~48个月尺度的标准化降水蒸散发指数(SPEI)和标准化降水指数(SPI),利用Pearson相关系数法、线性回归法和Mann-Kendall趋势检验法等方法研究了黄土高原地区植被对气象干旱的多时间尺度时空响应特征。结果表明:(1)1983—2015年,黄土高原植被状况整体趋于改善,但黄土高原整体干湿状况变化趋势不大。(2)黄土高原绝大部分区域植被变化与气象干旱指数呈现显著正相关关系,表明植被活动受到水分的限制较强。但是,过去几十年,黄土高原地区植被受到水分限制的影响程度有逐渐减轻的趋势。(3)黄土高原植被对短时间尺度的水分盈亏变化相对敏感,尤其是耕地和草地对1~4个月的SPEI更为敏感,而林地对SPEI的响应时间尺度较为分散。黄土高原VCI与SPEI、SPI的最大相关系数均主要出现在生长季(4—10月),表明水分条件在生长季对植被活动的影响较为显著。