Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks
This work addresses weather prediction for Bangladesh, which is incremental as it applies an existing LSTM method to new regional data.
The study tackled weather forecasting for Bangladesh by implementing an LSTM model on 115 years of data, achieving a mean error of -0.38°C for temperature and -17.64mm for rainfall in month-wise predictions.
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.