gaibernado/Flood-Prediction-and-Response-in-South-Sudan

This project was prepared by Gai Bernado, Dhieu Kiir and Allan John # Flood Prediction and Response in South Sudan ## CATEGORY: Computer Vision ### Team Members **Full Names** | **Role** | **Index No.** --- | --- | --- **Allan John Salesio** | Modular contributor and algorithm designer | 17-CIT-033 **Dhieu Kiir Yoll** | Software architecture designer | 17-CCS-061 **Gai Bernado Beliu** | Leader and code reviewer | 16-CCS-055 ### Refinement of Idea and Problem Articulation The project focuses on developing an **AI and deep learning-computer vision based system** for predicting and responding to natural disasters in South Sudan. Specifically, the aim is to create a predictive model that accurately forecasts disasters such as floods, droughts, and famines. The system utilizes data obtained from Google satellite imagery to facilitate efficient response mechanisms. The problem being addressed is the lack of advanced, reliable, and timely disaster prediction and response systems in South Sudan, exacerbating humanitarian crises during natural calamities. ### Division of Responsibilities - **Allan John Salesio**: Responsible for developing and fine-tuning predictive algorithms. - **Dhieu Kiir Yoll**: Tasked with designing the software architecture. This involves planning the system structure, ensuring scalability, and integrating various components of the project. - **Gai Bernado Beliu**: As the project leader, Gai will oversee the overall project progress, conduct code reviews, and ensure that the project milestones are met. He will also liaise with stakeholders and coordinate team efforts. ### System Architecture and Information Flow - **Inputs**: Historical data on weather patterns, geographical information, past disaster impacts, and real-time environmental data. - **Processing**: The AI model processes this data to identify patterns and predict potential disasters. The software architecture ensures seamless integration of data sources and processing modules. - **Outputs**: Predictive reports on potential disasters, severity, …