VRBagged-Net: Ensemble Based Deep Learning Model for Disaster Event Classification

Abstract

A flood is an overflow of water that swamps dry land. The gravest effects of flooding are the loss of human life and economic losses. An early warning of these events can be very effective in minimizing the losses. Social media websites such as Twitter and Facebook are quite effective in the efficient dissemination of information pertinent to any emergency. Users on these social networking sites share both textual and rich content images and videos. The Multimedia Evaluation Benchmark (MediaEval) offers challenges in the form of shared tasks to develop and evaluate new algorithms, approaches and technologies for explorations and exploitations of multimedia in decision making for real time problems. Since 2015, the MediaEval has been running a shared task of predicting several aspects of flooding and through these shared tasks, many improvements have been observed. In this paper, the classification framework VRBagged-Net is proposed and implemented for flood classification. The framework utilizes the deep learning models Visual Geometry Group (VGG) and Residual Network (ResNet), along with the technique of Bootstrap aggregating (Bagging). Various disaster-based datasets were selected for the validation of the VRBagged-Net framework. All the datasets belong to the MediaEval Benchmark Workshop, this includes Disaster Image Retrieval from Social Media (DIRSM), Flood Classification for Social Multimedia (FCSM) and Image based News Topic Disambiguation (INTD). VRBagged-Net performed encouraging well in all these datasets with slightly different but relevant tasks. It produces Mean Average Precision at different levels of 98.12, and Average Precision at 480 of 93.64 on DIRSM. On the FCSM dataset, it produces an F1 score of 90.58. Moreover, the framework has been applied on the dataset of Image-Based News Topic Disambiguation (INTD), and exceeds the previous best result by producing an F1 evaluation of 93.76. The VRBagged-Net with a slight modification also ranked first in the flood-related Multimedia Task at the MediaEval Workshop 2020.

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

“…The block diagram of the proposed visual module is shown in Figure 4. We have already published the DeepSDC visual module in [26].…”

“…Considering the above discussion, this paper proposes the Deep Social Media Data Classification (DeepSDC) approach for flood detection using visual and textual data, which is an extension of our top-ranked method presented in MediaEval 2020 and 2021 [18,26]. First, the images are processed using a combination of VGG16 [27] and ResNet50 [28] pretrained networks and text data is processed using RoBERTa [29] and XLNet [30] pre-trained models.…”

Water Quality Estimation and Anomaly Detection: A Review

“…Participants can tackle the task using text features, image features, metadata, or a combination of the above. You can review some papers using WaterMM benchmark dataset [97][98][99]. Using other social media platforms, collecting multimodal and cross-data will be the main focus of future works.…”

Customer Complaints-Based Water Quality Analysis

“…Participants in this work are provided with a collection of Twitter post IDs to download the text, the accompanying image, and the metadata of tweets that were selected using a keyword-based search that includes words or phrases about the quality of drinking water (e.g., strange color, odor, or taste, related illnesses, etc.). The challenge can be completed by participants using metadata, text features, picture characteristics, or a mix of the three [23,24].…”