  Temperature and rainfall have a significant impact on economic growth as well
as the outbreak of seasonal diseases in a region. In spite of that inadequate
studies have been carried out for analyzing the weather pattern of Bangladesh
implementing the artificial neural network. Therefore, in this study, we are
implementing a Long Short-term Memory (LSTM) model to forecast the month-wise
temperature and rainfall by analyzing 115 years (1901-2015) of weather data of
Bangladesh. The LSTM model has shown a mean error of -0.38oC in case of
predicting the month-wise temperature for 2 years and -17.64mm in case of
predicting the rainfall. This prediction model can help to understand the
weather pattern changes as well as studying seasonal diseases of Bangladesh
whose outbreaks are dependent on regional temperature and/or rainfall.
