Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms

The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machine learning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process.