Big Data and IOT-Based Flood Monitoring Using Deep Neural Network

Floods are natural disasters that have severe impacts on communities and ecosystems worldwide. Big data is used in training phase, and big data technologies have significantly improved our ability to monitor and respond to these natural disaster events. The application of Internet of Things (IoT) platforms to flood surveillance presents an opportunity to improve early warning, observation, and reaction to these occurrences. The IoT platform integrates a network of sensors, data collection devices, and communication infrastructure to monitor various environmental parameters that are critical for flood detection. The collected data from the sensors is transmitted in real time to a centralized IoT platform, which processes, analyzes, and visualizes the information. ResNet operates on image patches or segments, so adaptive image denoising is applied for accurate feature extraction. Apply the trained ResNet50 model to unseen or real-time data to classify them as chances of flood or not. This can involve feeding the data to the ResNet50 model and obtaining predictions or scores indicating the likelihood of flood conditions. The output can then be analyzed and monitored for decision-making. The representativeness of the features that the ResNet50 model captures and the quality and availability of labeled training data are the two main factors that determine how accurate using ResNet50 for flood monitoring will be.