Spatial Distribution of Drought Vulnerability Mapping: Introducing a new methodology

Abstract

Abstract Droughts as a natural calamity have wreaked havoc on human health, environment, and the economy around the world. Due to its complex and multi-faceted nature, the risk assessment of drought requires the analysis of diverse parameters and machine learning techniques provide an effective tool to approach this problem. In the present work, we have employed four machine learning models, Naïve Bayes (NB), Rotational tree- Forest by Penalizing Attributes (RF-FPA), Multi-Layer Perceptron (MLP), and Linear Discriminant Analysis (LDA) for the drought vulnerability mapping in the Najafabad watershed, Isfahan Province, Iran. The country faces serious challenge from hydrological and meteorological drought conditions. A total of 20 conditioning factors comprising of 3 topographical (slope, elevation, geomorphology), 6 environmental (NDVI, soil depth, LU/LC, soil texture, EC, soil moisture), 4 hydrological (groundwater level, drainage density, distance to stream, TWI), 4 meteorological (annual precipitation and temperature, evaporation, humidity), and 3 socio-economic (ADP, deep tune, population density) were included for the drought vulnerability mapping. The collinearity effects were checked with multicollinearity analysis prior to the spatial modelling. The variable importance of the different parameters was analysed using AdaBoost model. The results show that soil moisture is the most important parameters among all variables. It also came into the results that the RF-FPA, among all four models, is the most successful model during training (AUC = 0.976) and validation (AUC = 0.968).