Prediction of Flood Severity Level via Processing IoT Sensor Data Using a Data Science Approach

Abstract

River flooding is deemed a catastrophic phenomenon caused by extreme climate change and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of the Internet of Things (IoT), various types of sensing including social sensing, 5G wireless communication, and big data analysis has devised advanced tools for early prediction and management of distrust events. To this end, this article amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flooding. The article presents three river levels: normal, medium, and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations set up including training and testing of support vector machine and random forest using a principal-components-analysis-based dimension reduced dataset. In addition, we investigate the use of synthetic minority oversampling technique to balance the class representations within a dataset. As expected, the results indicate that a “balanced” representation of data samples achieved high accuracy (nearly 93 percent) when benchmarked with “imbalanced” data samples using random forest classifier 10-fold cross-validations.