Hasan, Md AbidMd AbidHasanRouf, Nirjhor TahmidurNirjhor TahmidurRoufHossain, Md SajidMd SajidHossainLatif, Lamia BintaLamia BintaLatifTasnim, AnikaAnikaTasnimGrzegorzek, MarcinMarcinGrzegorzek2024-05-152024-05-152023https://publica.fraunhofer.de/handle/publica/46788410.1109/ICTAI59109.2023.000282-s2.0-85182405885The involvement of a multitude of parameters adds to the complexity of modeling a flood. However, floods are among the most destructive of natural disasters and therefore, flood forecasting is one of the key priorities of hydrology. Flood forecasting goes a long way to minimize the loss of lives as well as economic losses. Furthermore, proper modeling of floods can contribute immensely towards future risk reduction and the introduction of necessary policies. At present, the application of machine learning in river and flood analysis has dramatically increased among hydrologists. In this research, we propose a location (District) independent flood prediction model (Random Forest-RF) of commercially significant rivers in Bangladesh. The data Imbalance problem is solved by synthetic minority oversampling of numerical and categorical (SMOTENC) data augmentation techniques. Our results show that the proposed framework outperforms the previously reported results by up to 9%. To the best of our knowledge, our proposed flood prediction framework achieved the best performance in terms of all evaluation matrices on the specific dataset.enData AugmentationElbow MethodFlood PredictionRandom ForestSMOTENCconference paper