Drought has been perceived as a persistent threat globally and the complex mechanism of various factors contributing to its emergence makes it more troublesome to understand. Droughts and their severity trends have been a point of concern in the USA as well, since the economic impact of droughts has been substantial, especially in parts that contribute majorly to US agriculture. California is the biggest agricultural contributor to the United States with its share amounting up to 12% approximately for all of US agricultural produce. Although, according to a 20-year average, California ranks fifth on the list of the highest average percentage of drought-hit regions. Therefore, drought analysis and drought prediction are of crucial importance for California in order to mitigate the associated risks. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index remains a challenging task. In the present study, we trained a Voting Ensemble classifier utilizing a soft voting system and three different Random Forest models, to predict the presence of drought and also its intensity. In this paper, initially, we have discussed the trends of droughts and their intensities in various California counties reviewed the correlation of meteorological indicators with drought intensities and used these meteorological indicators for drought prediction so as to evaluate their effectiveness as well as significance.

翻译：干旱已被视为全球范围内持续存在的威胁，而多种因素共同促成其发生的复杂机制使其更难以理解。在美国，干旱及其严重程度趋势同样备受关注，因为干旱造成的经济影响十分显著，尤其是在对美国农业贡献较大的地区。加利福尼亚州是美国最大的农业贡献州，其农产品约占全美总产量的12%。然而，根据20年的平均值，加利福尼亚州在受干旱影响区域平均比例最高的地区中排名第五。因此，干旱分析与干旱预测对于加利福尼亚州缓解相关风险至关重要。然而，基于干旱指数动态关系构建一致的干旱预测模型仍是一项具有挑战性的任务。在本研究中，我们训练了一个采用软投票系统和三种不同随机森林模型的投票集成分类器，以预测干旱的发生及其强度。本文首先讨论了加利福尼亚州各县的干旱及其强度趋势，分析了气象指标与干旱强度的相关性，并利用这些气象指标进行干旱预测，以评估其有效性和重要性。