Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks

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 stu...

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Bibliographic Details
Published in:arXiv.org 2020-10
Main Authors: Mohammad Mahmudur Rahman Khan, Md Abu Bakr Siddique, Shadman Sakib, Aziz, Anas, Ihtyaz Kader Tasawar, Hossain, Ziad
Format: Article
Language:English
Subjects:
Artificial neural networks
Economic development
Epidemics
Impact analysis
Meteorological data
Neural networks
Outbreaks
Pattern analysis
Prediction models
Predictions
Rain
Rainfall
Recurrent neural networks
Temperature dependence
Weather forecasting
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2010.11946