FLOOD HAZARD ESTIMATION AND EVALUATION IN LAGOS STATE USING MACHINE LEARNING TECHNIQUES

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Floods threaten human life, infrastructure, and economic stability in Lagos State, Nigeria. Accurate flood hazard estimation is crucial for effective flood risk management and mitigation. This study employs machine learning techniques to estimate flood hazards in Lagos State. A dataset comprising historical flood events, meteorological factors (rainfall, temperature, humidity), topographical features (elevation, slope, land cover), and socioeconomic variables (population density, urbanization) is compiled. Feature selection and engineering techniques are applied to optimize model performance. Flooding is the most frequent and destructive natural catastrophe that may happen anywhere in the globe. The frequency and severity of flooding events have increased worldwide in recent years due to climate change and human activity. Flooding has caused widespread death and devastation of property, farms, and vegetation in several emerging Nigeria, including Lagos State, and has forced the relocation of many more. Flooding has been Lagos State’s most common natural disaster during the last decade. Modern machine learning methods have shown great promise for improving flood Estimation and Evaluation. The optimum machine learning algorithm for flood Estimation and Evaluation is debated. To reduce the harm caused by floods, finding better ways to anticipate their occurrence is crucial. This paper initially applied 4 machine learning algorithms (Support Vector Machine SVM, Classification and Regression Trees CART, K-Nearest Neighbors KNN, and 4. Generalized Linear Model Network GLMNET) on the default dataset. The results reveal fair accuracy (over 60%) and kappa values (< 0.4). The same ML algorithms were again applied to the transformed dataset using the Boxcox transformation technique; the accuracy and kappa values improved but not significantly. Finally, Models for predicting floods were implemented using 3 different ensemble algorithms: Bagged CART (Bootstrap Aggregating BAG), Random Forest (RF), and Stochastic Gradient Boosting ( Gradient Boosting Machine GBM). Compared to the other three models, the performance of RF (Area Under the Curve AUC = 0.93) and BAG (AUC = 0.92) indicated superior accuracy.