Flood Prediction Using Machine Learning Models
It addresses flood forecasting for disaster management and policy-making, but appears incremental as it applies standard models without novel methodological claims.
This paper tackles flood prediction by applying machine learning models like Binary Logistic Regression, KNN, SVC, and Decision Tree Classifier to provide accurate forecasts, with a comparative analysis to determine the best-performing model.
Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progression of floods in real time is challenging. To estimate water levels and velocities across a large area, it is necessary to combine data with computationally demanding flood propagation models. This paper aims to reduce the extreme risks of this natural disaster and also contributes to policy suggestions by providing a prediction for floods using different machine learning models. This research will use Binary Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to provide an accurate prediction. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.