论文标题
使用长期记忆复发性神经网络预测孟加拉国的温度和降雨量
Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks
论文作者
论文摘要
温度和降雨对一个地区的经济增长以及季节性疾病的爆发产生了重大影响。尽管进行了不足的研究，但仍在进行了分析实施人工神经网络的孟加拉国的天气模式。因此，在这项研究中，我们正在实施长期的短期记忆（LSTM）模型，以通过分析孟加拉国天气数据的115年（1901-2015）来预测月份的温度和降雨。如果预测2年的月份温度，LSTM模型的平均误差为-0.38oC，则在预测降雨量的情况下为-17.64mm。这种预测模型可以帮助理解天气模式的变化以及研究孟加拉国的季节性疾病，其暴发取决于区域温度和/或降雨。
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.
