Real Time Flood Monitoring using Image Captioning

Abstract

One of the most destructive natural catastrophes, cataracts cause enormous losses in terms of people, property, and buildings. Detecting flood tide-prone areas is pivotal for effective disaster operation and mitigation strategies. Traditional styles similar as hydrological modelling and detector- grounded monitoring frequently bear expansive data and structure, which may not be readily available in all regions. This study proposes a new approach to descry flood tide-prone areas using image captioning ways. The proposed system analyses images captured near plages when water situations are raised and generates captions describing the scene. Key expressions in the generated captions, similar as" submerged land" or" high water position," are used to determine if an area is flood tide prone. The system leverages An integration of Long Short-Term Memory (LSTM) networks for caption creation and Convolutional Neural Networks (CNNs) for point birth. A threshold grounded decision- making algorithm is employed to classify areas grounded on the captions generated. Despite varying environmental conditions, our testing results show that the model achieves excellent delicacy in relating areas that are prone to flood tides. This approach is scalable, cost-effective, and can be integrated with drone or satellite imaging systems for large- scale flood tide threat assessment. likewise, the automated generation of threat dispatches makes the system practical for real- time operations in disaster operation.

Highlights:

Flood-Prone Area Detection: Implementation of image captioning models to identify flood-prone areas effectively.

Shoreline Image Analysis: Focused analysis of images captured near shores during periods of raised water levels.

Feature Extraction: Key visual features are extracted from visuals using Convolutional Neural Networks (CNNs).

Caption Generation: use of Transformer-based models or Long Short-Term Memory (LSTM) networks to produce illustrative captions.

Keyword-Based Risk Detection: Identification of key phrases in captions, such as "submerged land" or “high water level”, to assess flood risk.

Automated Decision Making: A threshold-based algorithm to classify areas based on captioned descriptions.

Risk Message Generation: Automated generation of alerts and messages indicating flood-prone areas.

Real-Time Applications: Potential integration with real-time imaging systems like drones and satellite cameras.

Scalable Solution: A scalable framework suitable for large-scale flood risk assessments in diverse environments.

Cost-Effective Approach: Reducing dependency on expensive sensors and extensive monitoring infrastructure.

Environmental Condition Adaptability: Accurate performance under varying weather and environmental conditions.

AI-Powered Disaster Management: Enhancing disaster management strategies through machine learning and AI technologies.

Data Augmentation Techniques: Use of data augmentation for improving the model's robustness against diverse image inputs.

Integration Possibilities: Future scope to combine the system with IoT and GIS for comprehensive flood risk mapping.

Improved Accessibility: Enabling flood risk detection for remote or underdeveloped regions with limited monitoring resources.