Abstract
Coastal regions have experienced economic losses from natural disasters due to rising global temperatures and climate change. Accurate and timely weather information may reduce people's sensitivity to the risks associated with climate change. In this context, the imperative for accurate weather forecasting is paramount as a proactive measure to mitigate the adverse consequences. Integrating weather forecasting into a user-friendly app or website can provide a vital communication channel, ensuring widespread awareness and preparedness by promptly disseminating information about imminent weather-related risks. The study aims to employ a two-layer stacking ensemble method customized for daily weather prediction, encompassing temperature, relative humidity, and sealevel pressure simultaneously through multioutput regression. The first layer of the model implemented a stacked feature scaling ensemble approach for the task of accurate prediction, whereas the second layer deployed a general stacking model. Sandwip, a southeasterly coastal zone in Bangladesh's Chittagong region, serves as the test site for weather parameters using daily data from 2007 to 2021 collected at the meteorological station. We conducted a comparative analysis of the proposed model and individual machine learning regressions as a potent tool to facilitate the enhancement of forecasting skills. Metrics such as coefficient of determination, mean absolute percentage error, mean squared error, and mean absolute error were used to assess the models. The result demonstrated that the proposed model demonstrated superior performance compared to other models in predicting each weather parameter. Precisely, the research obtained the most optimal R-squared values (0.945, 0.684, and 0.947) in predicting temperature, humidity, and sealevel pressure, respectively. A comprehensive analysis of extreme weather conditions was conducted based on forecasted weather variables, which is beneficial for the inhabitants of any coastal island in Bangladesh.
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Conceptualization, T. Mollick; investigation, T. Mollick and S. R. Sabuj; methodology, T. Mollick; project administration, T. Mollick, G. Hashmi, and S. R. Sabuj; software, T. Mollick; supervision, G. Hashmi, and S. R. Sabuj; validation, T. Mollick, G. Hashmi, and S. R. Sabuj; writing—original draft, T. Mollick; writing—review and editing, T. Mollick and S. R. Sabuj. All authors have read and agreed to the published version of the manuscript.
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Mollick, T., Hashmi, G. & Sabuj, S.R. A multifaceted journey in coastal meteorological projections through multioutput regression: a two-layer stacking ensemble approach. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04923-9
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DOI: https://doi.org/10.1007/s00704-024-04923-9