Natural calamities like drought cause misery to human lives as well as environment in a variety of ways. The huge adverse consequences and globally predicted climate change accentuate the importance of an effective drought assessment and management system. Lack of universality of available drought indices emphasize the need of an automated system that works globally. Internet of Things (IoT) is highly appropriated for ubiquitous monitoring, acquisition and evaluation of causing parameters for reliable prediction of drought situations. The framework includes dimensionality reduction algorithm at Fog layer through which only variance rich data passes. This data is evaluated at the Cloud layer to determine the drought severity level employing Artificial Neural Network (ANN), ANN optimized with Genetic Algorithm (ANN-GA), DNN (Deep Neural Network) and their performance is compared. Support Vector Regression (SVR) method predicts the drought conditions for three different climate blocks and three different time frames. Experimentation reveals that DNN outperformed with high accuracy, sensitivity, specificity, precision, f-measure with values 95.361%, 91.584%, 96.834%, 91.857%, 91.72% respectively and with effective execution time.

诸如干旱之类的自然灾害以各种方式给人类生活和环境造成痛苦。巨大的不利后果和全球预测的气候变化凸显了有效的干旱评估和管理系统的重要性。现有干旱指数缺乏通用性，这强调了在全球范围内运行的自动化系统的需求。物联网（IoT）非常适用于普遍监测，获取和评估导致参数的情况，以可靠地预测干旱情况。该框架包括雾层的降维算法，只有方差丰富的数据才能通过该算法。使用人工神经网络（ANN），通过遗传算法（ANN-GA）优化的人工神经网络，在云层评估此数据以确定干旱的严重程度，比较了DNN（深度神经网络）及其性能。支持向量回归（SVR）方法可预测三种不同气候区和三种不同时间范围的干旱条件。实验表明，DNN的准确率，灵敏度，特异性，精确度和f度量的性能均优于95.361％，91.584％，96.834％，91.857％，91.72％，并具有有效的执行时间。