With the increasing impact of natural disasters, people have gradually begun to pay attention to disaster risk and incorporate risk management into various disaster reduction actions to reduce disaster losses more effectively. In order to solve the shortcomings of the existing research on dynamic assessment of agricultural drought risk, based on the discussion of the steps of dynamic assessment of drought risk and the components of drought risk, as well as BP neural network and random forest algorithm, this paper briefly discusses the indicators and weights of drought risk assessment and the establishment of empirical data samples, and discusses the design of dynamic assessment model of agricultural drought risk. Finally, the random forest evaluation model designed in this paper is compared with BP neural network and variable fuzzy controller. The experimental results show that the accuracy of random forests in assessing the regional risk level of drought is 97.3% on average. The recognition accuracy of random forest and BP neural network is better than that of variable fuzzy controller. Therefore, it is verified that the agricultural drought risk dynamic assessment method based on machine learning has high practical value.

Kothapa Lakshmi. Dynamic Assessment of Agricultural Drought Risk Based on Machine Learning. Machine Learning Theory and Practice (2022), Vol. 3, Issue 1: 44-53. https://doi.org/10.38007/ML.2022.030106.

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