Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

Abstract

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for earlywarning and disaster alleviation. In this paper, SPEI-1was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vectormachine (SVM)modelwere established. Meteorological data and remote sensing datawere used to derive the predictionmodels. The results showed the following. (1) The SVM model performed the best when themodelswere developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVMmodel driven by the combinedmeteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the singlemeteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.