Predicting Disasters: A Machine Learning Approach

Trends in river water levels that could cause floods are an intriguing and applicable subject of study. They set up shop to lessen the financial and social impact of floods. Water level variations can be predicted using support Vector, a class of machine learning methods that can also be used to detect the likelihood of flooding. The two algorithms use comparable hydrological and flood resource variables in the process of predicting floods, such as the prediction amount, River inflow, seasonal flow, flood frequency, and other pertinent flood prediction variables, because the water level is the most important factor in hydrological research. Machine learning techniques are helpful for predicting floods because they have the ability to classify and regress data from several sources into groups of flood and non-flood. Anyone may use the Machine Learning and Deep Learning features of artificial intelligence thanks to Watson Studio.