Constructing with River Flood Prediction Models

Abstract

With the incidence of severe weather and flooding on the increase around the world, there is a need to improve flood forecasting and warning. Floods cause physical damage, loss of basic sanitation that leads to disease, economic hardship due to rebuilding costs and food shortages. By improving flood forecasts it becomes possible to take mitigating actions in advance of the flood and hence avoid millions of pounds worth of damage and even human fatalities. In this paper, a time series and Markov models for river flood prediction are constructed. These models focus on the prediction of events and can capture the fact that time flows forward. The output will be approximate and show that there is a close agreement between the predicted and actual river flooding amount. The system compares the results of time series model and Markov model with the actual weather station results and also shows the best model for river flood prediction over Ayeyarwady River in Myanmar.