Flood is one of the devastating natural disaster than anything else. It is a harmful event that can risk human life, damage homes, and have huge economic impacts. Flooding creates garbage and solid waste which includes dead animals, waste products, etc. and this can increase the possibilities of spreading disease and worsening water and sanitation problems in an area and hence the need to warrant a rapid response. Several approaches of waste classification have been proposed by various researchers but only some few researches concentrate on classifying flood waste. In this study, a hybrid flood waste classification model using a 3D-wavelet transform (3D-DWT) and Support Vector Machine (SVM) was developed to address these challenges. 3D-DWT transforms the preprocessed input data by providing a time–frequency representation of the signal in different time periods in the 3D wavelet time domain, and also provides important information about the physical structure of the data and extracts the features from the main signal which serves as input to the SVM classification. The image dataset from Kaggle was classified into recyclable or non-recyclable. A total of 400 images were used to test the model to evaluate the performance and an accuracy of 85.25% and 86.00% for SVM + 2D and SVM + 3D models respectively after the model was tested with adequate times iteration. Comparing with related works which only uses the 2D-DWT and a single model, an hybrid 3D-DWT and SVM was used where 3D-DWT decomposes the images, while prediction is done using SVM thereby improving the accuracy of the system.

洪水是最严重的自然灾害之一。这是一种有害事件，可能危及人类生命，破坏房屋并产生巨大的经济影响。洪水会产生垃圾和固体废物，其中包括死亡的动物，废物等，这可能增加疾病传播的可能性，并加剧该地区的水和卫生问题，因此需要做出快速反应。各种研究人员已经提出了几种废物分类方法，但是只有很少的研究集中在洪水废物分类上。在这项研究中，开发了使用3D小波变换（3D-DWT）和支持向量机（SVM）的混合洪水废物分类模型，以解决这些挑战。3D-DWT通过提供3D小波时域中不同时间段内信号的时频表示来转换预处理的输入数据，还提供有关数据物理结构的重要信息并从主信号中提取特征用作SVM分类的输入。来自Kaggle的图像数据集分为可回收或不可回收。总共使用了400张图像对模型进行测试，以在对模型进行适当的时间迭代测试之后，评估SVM + 2D和SVM + 3D模型的性能和准确度分别为85.25％和86.00％。与仅使用2D-DWT和单个模型的相关作品相比，混合使用了3D-DWT和SVM，其中3D-DWT分解了图像，