Application of Artificial Intelligence Algorithms to Control the Use of Flood-prone Areas

Abstract

This article examines the possibility of using artificial intelligence tools to analyze the use of territories prone to flooding during floods. A modern system for monitoring the economic use of flood-prone areas should be based on the use of Earth remote sensing data. The analysis of satellite images, being a laborious task, can be automated through the use of specially trained convolutional neural networks of semantic segmentation based on the algorithm proposed in this article. In this work, on the previously identified flooding zones, using remote sensing data, development objects are automatically determined (segmented) for different times and, by combining information at different times, an assessment of the intensity of this construction in the inter-flood period is made. To form a training sample, a survey of several settlements in the Trans-Baikal Territory was carried out using unmanned aerial vehicles. The neural network was configured using the Python language and the PyTorch library. To select the best convolutional neural network configuration, various combinations of architectures and encoder types were tested for performance and accuracy. The best result in terms of speed and accuracy was shown by the U-Net architecture, built using a convolutional neural network with an SE-ResNeXt50 encoder. According to satellite images of high spatial resolution for the Aginskoye village of Trans-Baikal Kray, a development map was drawn in the flood hazardous area in 2013 and 2019. The objects of development in the period between floods were identified. The results of the study can make it possible to consider a number of important factors when planning the rational use of flood-prone areas in order to improve the quality of life in the region. The obtained maps of the development of flood-prone zones of a large spatial scale are planned to be recommended in the work of state authorities in the field of water resources protection and elimination of natural disasters.