Prediction of Flood in Bangladesh Using Different Classifier Model

Abstract

Bangladesh is highly affected by climate change
scenarios notably floods due to its location on the world map in the
South Asian region. Besides due to monsoon rains and high
upstream rainfall in several areas eventually turn into floods.
Thus, early flood forecasting might save human lives as well as
agriculture crops. In this paper, we have applied different machine
learning classifier models (Decision tree, Naive bayes, k-NN and
Random Forest) with a view to predicting the occurrence of flood.
RapidMiner tool has been used extensively to perform data
preparation, decision tree model generation, cross-validation,
model selection and optimization of the model parameters. It is
seen that the decision tree model has performed well by achieving
an accuracy of 94.23% which is further optimized to reach
94.68%. Comparison of four widely used optimized classifier
models as well as the feature selection criterion using ‘correlation
matrix’ is also a good aspect of this work by which we have reached
to a good result.
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