FLOOD PROGNOSIS USING AGGREGATION MACHINE LEARNING STRUCTURE

Frequent and devastating floods in Lagos State pose a significant threat to people and property. Accurate and real-time forecasting of floods is essential to mitigate their impact. This thesis focuses on evaluating different machine-learning structures for flood prediction in Lagos State. The structures assessed include K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Binary Logistic Regression, and Stacked Generalization (Stacking). The researchers trained and tested these structures using a rainfall dataset. The results demonstrate the better results of the stacked generalization model than the others, achieving an impressive accuracy of 93.3 per cent with a standard deviation(sd) of 0.098. These findings highlight the potential of machine learning models to provide precise and timely flood predictions, empowering the local authorities, especially disaster management ones, to take necessary actions to avoid destruction and preferably save people. Floods pose significant threats to human life, infrastructure, and economic stability. Timely and accurate flood predictions are crucial for effective disaster management. This study proposes an innovative aggregation machine learning structure to enhance flood prognosis accuracy. By integrating multiple machines learning algorithms, including random forests, support vector machines, and artificial neural networks, our framework leverages the strengths of individual models to improve predictive performance. The proposed framework is evaluated using a comprehensive dataset of hydrological and meteorological factors. Results demonstrate significant improvements in flood prediction accuracy, with a reduction in false positives and false negatives. The aggregation structure outperforms individual models, achieving an accuracy of [insert percentage] and a mean absolute error of [insert value]. This research contributes to the development of reliable flood prediction systems, enabling proactive measures to mitigate flood risks and protect vulnerable communities.