Abstract
In this study, a new optimized comprehensive drought index system (OCDIS) was developed based on pressure-state-response (PSR) and random forest (RF). Then the pressure, state, response, and integrated agricultural drought risk were evaluated according to the synthetic-weight variable fuzzy set (SW-VFS) model. Finally, the countermeasures in terms of pressure, state, and response were discussed. The proposed index has been implemented in Qujing, Yunnan Province, China. The results showed that of the 10 indices included in the OCDIS, the four most important indices for agricultural drought risk management are reservoir storage capacity, precipitation anomaly percentage, soil moisture, and per capita annual income. The pressure risk and response risk of Malong are relatively higher than other counties. The integrated results indicated that most counties of Quijng have moderate drought risk. The assessment results are consistent with the actual situation of Qujing. The proposed model provides a scientific and objective way to develop the risk index system of agricultural drought. This study can potentially assist government agencies with information on the most important drought impacts and provide the basis for science-informed decision-making.
Highlights
Drought is a water shortage phenomenon that affects agricultural production, food safety, social stability, and ecological harmony [1,2]
Considering the difference between data and the contribution of each data to the results, synthetic-weight based on entropy and random forest was developed to determine the weight of variable fuzzy sets (VFS), and establish the synthetic-weight variable fuzzy set (SW-VFS) model
The PSR model was adopted to construct the original index system of agricultural drought risk, i.e., the input variables of random forest (RF); the RF model was used to measure the importance of each index, and to develop the optimized comprehensive drought index system (OCDIS)
Summary
Drought is a water shortage phenomenon that affects agricultural production, food safety, social stability, and ecological harmony [1,2]. The purpose of this study is to construct an optimized comprehensive drought index system (OCDIS) from a number of input variables based on the random forest model. The input variables of the RF model are named as the original index system of drought risk assessment. Considering the difference between data and the contribution of each data to the results, synthetic-weight based on entropy and random forest was developed to determine the weight of VFS, and establish the synthetic-weight variable fuzzy set (SW-VFS) model. The motivation of the paper is to construct an optimized comprehensive drought index system (OCDIS); focus on the established index system and synthetic-weight variable fuzzy set (SW-VFS) model to evaluate the agricultural drought risk (ADR); and analyze the ADR of Qujing
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