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 early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were 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 SVM model driven by the combined meteorological 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 single meteorological 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. HIGHLIGHTS SPEI-1 was used to analyze the temporal distribution characteristics of drought and the main driving factors in Guanzhong Area, China.; Drought grades were selected as the dependent variable, and the meteorological, geographical and vegetative factors were selected as the independent variables to establish an autoregressive integrated moving average (ARIMA) model, random forest (RF) model and support vector machine model.; Meteorological data and remote sensing data were used as independent variables to derive prediction models, respectively.; Comparing the models driven by remote sensing data only and the combination of meteorological and remote sensing data, the RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in Guanzhong Area.; This study can provide an important scientific basis for regional drought warning and prediction.;

Keywords

• drought prediction
• integrated autoregressive moving average model
• random forest model
• standardized precipitation evaporation index
• support vector machine model