Abstract
Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.
Highlights
A flash flood is caused by heavy rain associated with a severe thunderstorm, hurricane, etc. which are physical phenomena occurring in rapid flooding of low-lying areas such as plains, rivers, and dry lakes
We propose a novel Deep Learning architecture, namely PSO-UNET, which combines the Particle Swarm Optimization (PSO) with the UNET model to improve the performance of the flash flood detection from satellite images
While the and accuracy score of the deep Neural Network (NN) for classification problem considered as the crucial criteria, and semantic segmentation hasparts, two most imporTheisUNET’s architecture is symmetric comprises of two main a contracting tant criteria, are thepath discrimination at pixel level and to project path and anwhich expanding which can be widely seen asthe an mechanism encoder followed by athe dediscriminative features learnt at different stagesscore of theof contracting onto the pixel(NN)
Summary
A flash flood is caused by heavy rain associated with a severe thunderstorm, hurricane, etc. which are physical phenomena occurring in rapid flooding of low-lying areas such as plains, rivers, and dry lakes. In order to detect the flash flood from satellite images, various Machine Learning (ML) methods were presented in the literature. Sudhishri et al [6] compared the evaluation of ANN and Recurrent Neural Network (RNN) based flash flood models. The papers showed the application of deep NN in various fields such as manufacturing, power, but there were no one which applies into the flash flood fields such as the classification and segmentation problems. The papers are instances of the application of the Deep Learning model in the self-driving field, so that it is necessary to mention to the articles used for the flash flood classification. We propose a novel Deep Learning architecture, namely PSO-UNET, which combines the Particle Swarm Optimization (PSO) with the UNET model to improve the performance of the flash flood detection from satellite images.
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