MAE(μg/m3) value (a) for Banqiao (b) for Cailiao.

<div>In many deep learning tasks, it is assumed that the data used in the training process is sampled from the same distribution. However, this may not be accurate for data collected from different contexts or during different periods. For instance, the temperatures in a city can vary...

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Bibliographic Details
Main Author:	Md Khalid Hossen (20706979) (author)
Other Authors:	Yan-Tsung Peng (20706982) (author), Meng Chang Chen (12551601) (author)
Published:	2025
Subjects:	Medicine Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified neural network models various unclear reasons proposed two models highest drifting stations identify data drifting data drifting scenario data drifting problem data drifting issue loaded connection model baselines bilstm model previous year ’ div >< p accurate results compared data drifting various stations sub >< proposed front weather data data used data collected comparing data experimental results accurate results model training cnn model year due training process th </ term memory specific locations significant drift p values p value mape metrics long short highlight areas five years different periods different contexts ></ sub
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MAE(<i>μg</i>/<i>m</i><sup>3</sup>) value (a) for Banqiao (b) for Cailiao.

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