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Project # 35
Evaluating Flood Prediction Accuracy using Satellite Data

Team Lead: Saurya Pandey
Other Team Members: Ruby Soriano | Analisa Gonzalez
Faculty Advisor(s): Dr. Adnan Rajib
Department: CE
Project Type: Other Undergraduate
Title: Evaluating Flood Prediction Accuracy using Satellite Data
Abstract: Accurate flood prediction is essential for effective disaster preparedness, response, and water resource management. This study evaluates the accuracy of flood prediction using satellite-derived data by applying a contingency matrix approach. Satellite observations provide spatially extensive and timely information that can support flood monitoring and early warning systems, particularly in regions where ground-based measurements are limited. In this analysis, predicted flood events derived from satellite data are compared with observed flood occurrences to assess model performance. A contingency matrix is used to classify results into four categories: hits (correctly predicted floods), misses (observed floods not predicted), false alarms (predicted floods that did not occur), and correct negatives (correctly predicted non-flood events). From this matrix, standard performance metrics such as probability of detection, false alarm ratio, and overall accuracy are calculated to quantify predictive reliability. The results provide insight into the strengths and limitations of satellite-based flood prediction methods and highlight areas where prediction models can be improved. By systematically evaluating prediction outcomes, this study contributes to the development of more reliable flood forecasting tools. Improved prediction accuracy can enhance early warning capabilities, support risk mitigation strategies, and assist decision-makers in managing flood hazards more effectively.
Poster: View Poster #35 (PDF)
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