Abstract
A new model for risk assessment of drought based on projection pursuit optimized by immune evolutionary algorithm and information diffusion method (IEAPP-IDM) was proposed. Due to the fact that drought risk assessment is a complex multicriteria and multilevel problem, the IEAPP-IDM model can project the multidimensional indicators of samples into one-dimension projection scores; then, the information carried by the projection scores was diffused into drought risk levels; finally, the drought disaster risk estimate was obtained. In the present study, Qujing was employed to assess the drought risk with the proposed model. The results showed that Xuanwei possessed higher risk, while Luliang and Zhanyi possessed lower risk. At the same time, the probability risk of drought in Malong and Luoping was increasing, while the probability risk of drought in in Qilin and Shizong was decreasing. The results obtained by the assessment model are consistent with the actual situation of Qujing and verify the model’s effectiveness. The study can provide scientific reference in drought risk management for Qujing and other places of China.
Highlights
In recent years, extreme climate events, especially droughts, have occurred more frequently and caused large amounts of loss [1,2,3]
China is one of the countries that is most afflicted with serious drought disasters in the world
The study area is located in the east of Yunnan Province and is influenced by East Asian monsoon and South Asian monsoon, resulting in fluctuating precipitation [5,6,7]
Summary
Extreme climate events, especially droughts, have occurred more frequently and caused large amounts of loss [1,2,3]. China is one of the countries that is most afflicted with serious drought disasters in the world. The annual average economic losses caused by droughts in China are over tens of billions of dollars from 2006 to 2014 [4]. Drought has become an important factor constraining China’s agricultural productivity and sustainable development. From 2009 to 2012, droughts had affected 453628.9 hectares of farmland and 3.48 million people in this area [8]. Evaluating drought risk is important for improving the prediction of drought and reducing the losses in the region
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