Flash floods prediction model: a case of Nairobi

Abstract

This study developed a flash flood prediction model for Nairobi, focusing on improving the city's resilience and emergency preparedness and response to flash floods. The study employed quantitative and experimental research design. A machine-learning model was built to predict flash flood occurrences using historical rainfall, soil moisture, and meteorological, hydrological, and topographical data. The key variables identified to influence flash flood occurrence were rainfall, soil moisture content, river discharge, and erosion degree, with rainfall showing the highest correlation (61%) to flash floods. Among various machine learning models, the Random Forest model outperformed others with an accuracy of 93.33%, recall of 90.47%, and an F1 score of 0.90, making it the most reliable predictor. Other models, such as KNN, Logistic Regression, SVM, and ANN, also showed impressive performance. The developed model can potentially improve flood prediction which can lead to reduction on damages, enhance Nairobi's resilience to flash floods, and providing a reference for other urban areas facing similar climate challenges. It was recommended that Nairobi and similar metropolitan areas should invest in enhancing their drainage infrastructure to complement the predictive model's capabilities. Integrating this model into city planning, emergency response systems, and early warning systems could help in mitigating the risks posed by flash floods. Additionally, policymakers should prioritize land use planning and environmental conservation to address the key drivers of flash floods, such as soil erosion and improper land use.