Implementing machine learning techniques to forecast floods In Bangladesh based on historical data

Abstract

Flooding is a complex phenomenon that, due to its nonlinear and dynamic character, is difficult to anticipate. As a result, the prediction of floods has emerged as a critical area of study in the field of hydrology. Numerous researchers have handled this topic in various ways, spanning from physical models to image processing, however, the time steps and precision are insufficient for all applications. This report looks at machine learning approaches for forecasting weather conditions and criteria and assessing the related margins of uncertainty. The evaluated outputs enable more accurate and precise flood prediction for a variety of applications, including transportation systems. Through the exploration of innovative approaches to flood forecasting, machine learning algorithms have emerged as a potential solution. Up-and-coming methods, including ANNs, SVMs, and Random Forests, have shown impressive performance in identifying intricate patterns and connections in both weather and hydrological data. By leveraging past weather and water information, these algorithms can generate advanced predictions of future conditions and anticipate possible flood occurrences. Responding to emergency scenarios can be made more efficient and beneficial by exploiting machine learning capabilities and advanced sensor data to more accurately predict and prepare for the devastation caused by floods, and more easily deliver aid to flood affected regions.