Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy-efficient flood prediction and forecasting system. IoT sensor nodes are constrained in battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system’s power consumption. Cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e., water flow and water level, are being investigated for India’s Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. ANN (Artificial Neural Network) algorithm is used to predict the flood, and the simulation technique of Holt Winter is used to forecast the future flood. Data are obtained from the Indian government meteorological database, and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.

洪水是一种反复出现的重要自然现象，几乎影响到整个地球。这是一个严重的问题，会导致作物破坏、人口破坏、基础设施损失和一些公用事业的拆除。解决这个问题的一个有效方法是提醒社区注意即将到来的洪水，并提供充足的时间来疏散和保护财产。在本文中，我们提出了一个基于物联网的节能洪水预测和预报系统。物联网传感器节点受到电池和内存的限制，因此雾层使用基于数据异构性的节能方法来保持系统的功耗。云存储用于高效存储。环境条件，如温度、湿度、降雨量和水体参数，即水流量和水位，正在调查印度喀拉拉邦地区的洪水阶段。PCA（主成分分析）方法用于雾层进行属性降维。洪水预报采用人工神经网络（ANN）算法，未来洪水预报采用霍尔特冬季模拟技术。数据取自印度政府气象数据库，并进行实验评估。研究结果表明了所提出的架构的可行性。数据取自印度政府气象数据库，并进行实验评估。研究结果表明了所提出的架构的可行性。数据取自印度政府气象数据库，并进行实验评估。研究结果表明了所提出的架构的可行性。