Advanced Techniques of Flood Forecasting, Flood Inundation Mapping and Flood Prioritization of Panam River Basin

Abstract

Floods have been quite possibly the most troubling natural disasters in history, causing significant casualties, loss of life, and collateral destruction. Also, we see a shift in the frequency of rainfall every year due to climate change, which exacerbates flooding. Flood forecasting is essential to provide early warning to the people of flood-prone areas, provide enough time for preparedness, and reduce the damage to lives and properties. The flood inundation simulation is critical in presenting potential impending flooding in the study region. Furthermore, flood prioritization plays a key role in better watershed management. Looking at the present scenario, the machine learning methods like neural networks and fuzzy logic contribute profoundly to the headway of flood forecast frameworks giving better execution and financially savvy arrangements. The flood forecasting models can be developed using ANN, ANFIS and fuzzy logic. The comparative study between the developed models can also be carried out by determining different evaluation parameters. For flood forecasting using ANFIS, it is found that the coefficient of correlation values ranges from 0.85 to 0.95. In order to regulate the extent of the flooded area and the depth of the flooded water, HEC-RAS efficiently develops a flood inundation map. Flood inundation maps can be used to know the regions which are more or less vulnerable to flooding hazards.