Abstract:
Flooding occurs when a water body engulfs the mainland, disrupting the regular lives of the
inhabitants. Bangladesh faces a persistent risk of flooding due to its geographical location and the
impacts of climate change. Annual floods consistently disrupt the ordinary rhythm of life in the
country, causing the impoverished residents to lose their homes, crops, and, tragically, loved ones.
This ongoing threat renders these individuals more susceptible. In our study, we gathered a
comprehensive set of climate data spanning 74 years, ranging from 1949 to 2022, from the
Bangladesh Meteorological Department. This research aims to alleviate the destructive impacts of
flooding by employing ensemble machine learning techniques. We have used two ensemble
approach Bagging and Stacking and utilize two models for each approach. Different combination
of six algorithms namely Decision Tree, Random Forest, Xtreme Gradient Boosting, AdaBoost,
Support Vector Machine and Logistic Regression are used to develop those four models. All our
four model demonstrate robust performance for predicting flood in different regions of
Bangladesh. Our highest accuracy is obtained 97.22% with the Bagging approach model where
we have used Decision Tree, Random Forest, Xtreme Gradient Boosting as base classifier. The
precision, recall, F1-score, and ROC-AUC for this model were respectively 0.92, 0.91, 0.92 and
0.95. We have also evaluated Matthews Correlation Coefficient (MCC) and Brier Score. Those are
respectively 0.9 and 0.028. These results signify the potential of our model to play a significant
role in flood forecasting, showcasing its effectiveness in predicting and mitigating the impact of
floods
