Utilizing Random Forest Machine Learning Models to Determine Water Table Flood Levels through Volunteered Geospatial Information

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Authors

Raghav Sriram 

Abstract

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. Thus, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities

DOI

https://doi.org/10.31223/X5QS4C

Subjects

Applied Mathematics, Computer Sciences, Earth Sciences, Environmental Sciences, Physical Sciences and Mathematics

Keywords

volunteered geogrpahic information, vgi, water table detection, flood prevention

Dates

Published: 2021-04-27 11:50

License

CC0 1.0 Universal - Public Domain Dedication

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Data will not be avaliable for public access as consent form ensured participants data would not be shared.

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