Chaki Shuchi, Hasan Mehedi
Department of Applied Mathematics, University of Dhaka, Bangladesh.
Heliyon. 2024 Jul 6;10(14):e34253. doi: 10.1016/j.heliyon.2024.e34253. eCollection 2024 Jul 30.
In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011-2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.
在本研究中，已尝试探讨使用机器学习模型——人工神经网络（ANN）对达卡市气温进行季节性预测的可能性。温度的先验知识至关重要，尤其是在达卡这样的热带地区，因为它有助于预测热浪并实施有效的防范方案。虽然世界各地已采用各种机器学习模型来预测炎热天气，但专门针对孟加拉国的研究有限。此外，机器学习模型的应用需要根据任何地区的特定天气特征进行调整。因此，本研究采用人工神经网络方法预测达卡的气温，探索相关天气变量的潜在作用。本研究利用从国家环境预测中心/国家大气研究中心（NCEP/NCAR）再分析数据（0.25°×0.25°全球网格）收集的2011 - 2020年2月至7月的每日数据，着重寻找预测气温时天气变量的组合。人口密集的达卡市近年来因极端气候条件面临了严重后果，本研究将为该主题的进一步研究开辟新的维度。
