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.
