近年来，在气候变化的背景下，海洋和沿海生态系统经历了复杂的环境变化。这些变化还反映在通过水环境传播的疾病中，因为这些疾病的某些病原体在水生环境中无处不在，并且它们的存活率受到气候条件变化的影响。先前的研究已经在基本气候变量与霍乱弧菌的沿海分布和季节动态之间建立了牢固的关系，霍乱弧菌的致病类型与人类霍乱疾病有关。在这项研究中，研究者们提供了一种新颖的预测方法，该方法可以利用大气、陆地和海洋卫星衍生的基本气候变量来预测印度沿海地区的环境霍乱风险，该地区有2亿多居民。在2010-2018年期间，针对印度沿海地区的霍乱暴发数据，开发、训练和测试了随机森林分类器模型。随机森林分类器模型的准确性为0.99，F1得分为0.942，敏感度得分为0.895，这意味着可以正确识别89.5％的暴发。

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified.