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

Abstract

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

Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

“…Satria et al [4] developed a system to predict flood depth in Manila city using K-nearest neighbors (KNNs) and inverse-distanceweighted interpolation (IDW). Wasiq et al [5] combined machine learning models and data analysis methods with Internet of Things (IoT) sensor data to predict flood risk levels, but the prediction results were intervals, which can only provide vague reference results. Du et al [6] used Soil Moisture Active Passive (SMAP) and Landsat monitoring to evaluate and predict flood inundation, and achieved good results, but the model used was complex and difficult to implement.…”