Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan.
Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia.
Sensors (Basel). 2021 Apr 9;21(8):2648. doi: 10.3390/s21082648.
Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.
自然灾害不仅扰乱了人类生态系统，还破坏了人类社会的财产和关键基础设施，甚至导致生态系统永久改变。灾害可能是由地震、气旋、洪水和野火等自然发生的事件引起的。许多深度学习技术已经被不同的研究人员应用于检测和分类自然灾害，以克服生态系统的损失，但由于图像的复杂和不平衡结构，自然灾害的检测仍然面临问题。为了解决这个问题，我们提出了一种多层深度卷积神经网络。该模型由两个模块组成：I 型卷积神经网络 (B-I CNN)，用于检测和发生灾害，以及 II 型卷积神经网络 (B-II CNN)，用于使用不同的滤波器和参数对自然灾害强度类型进行分类。该模型在 4428 张自然图像上进行了测试，并计算了性能并表示为不同的统计值：敏感性 (SE)，97.54%；特异性 (SP)，98.22%；准确率 (AR)，99.92%；精度 (PRE)，97.79%；和 F1 分数 (F1)，97.97%。整个模型的整体准确率为 99.92%，与最先进的算法具有竞争力和可比性。
