Integration of AI and Software Engineering for Disaster Management

Artificial intelligence (AI) has emerged as a powerful tool in the realm of disaster management and software engineering, revolutionizing the way we prepare for, respond to, and recover from natural and man-made disasters. AI-driven solutions have become essential in safeguarding lives, minimizing damage, and optimizing resource allocation during crises. In an era marked by heightened climate change impacts and urbanization risks, the integration of AI in software engineering revolutionizes response to a disaster. Utilizing AI's capabilities of data analysis, predictive modeling, and automation enables efficient preparedness, coordinated response efforts, and streamlined recovery. For instance, the critical information shared rapidly in microblogging platforms such as Twitter (X) serves a data for the development of automated systems to filter actionable insights for timely humanitarian responses. This chapter explores the integration of AI in software engineering for disaster management with a focus on disaster information identification using both textual and visual data synergistically. In contrast to standard approaches, we provide a multimodal catastrophe detection method that effectively learns from both photos and text data to categorize catastrophe damages. Unlike other efforts that primarily analyze textual modality or use the most prevalent RNNs (recurrent neural networks), our technique seeks to avoid possible performance loss caused by extended input sequences. In our proposed method, pretrained CNN (ResNet), attention mechanism, and the bidirectional long short-term memory (Bi-LSTM) model were used to handle visual and textual data, respectively. The early fusion strategy is utilized to fuse the properties of image and text. Furthermore, this research investigates several visual (VGG19, Inception) and textual (Bi-LSTM, Bi-LSTM+ attention) models for multimodal baseline evaluation. In addition to this, the present work explores the effect of different fusion techniques such as early fusion and late fusion on the performance of the proposed approach. The proposed approach is evaluated on the open-source dataset named “Multimodal damaged identification for humanitarian computing” containing 5831 image–tweet pairs with 5247 instances for training and 584 instances for testing. The dataset contains 6 classes namely damage nature, damage infrastructural, human-damaged, fires, flood, and ND. The standard performance evaluation measures such as precision, recall, and F1 score demonstrate the suggested multimodal system improves performance above the existing baselines. In our proposed system, the best-performed model is ResNet50 with an LSTM+ attention mechanism. The proposed best model with early fusion yields a result of 90% accuracy, while late fusion achieves 1% improvement resulting in 91% accuracy. Our proposed approach, based on a multimodal AI algorithm for disaster identification, has the scope for software application in disaster management. This not only emphasizes the immediate benefits of our methodology but also underscores the broader potential for proactive disaster management solutions.