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Hi!这里是威力斯的个人主页。最近觉得做这么一个网页挺有意思,于是便有了你现在看到的这些内容。我打算在这里分享我觉得有趣的想法或者事情。
我现在在岐阜大学做研究员和助教的工作。主要研究方向是遥感,地理信息系统,水文学以及深度学习。如果有兴趣,可以点击左侧ORCID或者ResearchGate图标来了解更多关于学术方面的信息。
作为兴趣爱好我喜欢摄影和视频剪辑。欢迎从左侧图标访问我的B站主页以及微信公众号。此外我也喜欢试着用Python写一些有趣的程序(虽然还是个菜鸟),如果你有什么好的想法,欢迎与我分享。
博士(工学), 2022
岐阜大学
硕士(应用生物科学), 2015
岐阜大学
学士(地理科学), 2012
内蒙古师范大学
Missing observational data pose an unavoidable problem in the hydrological field. Deep learning technology has recently been developing rapidly, and has started to be applied in the hydrological field. Being one of the network architectures used in deep learning, Long Short-Term Memory (LSTM) has been applied largely in related research, such as flood forecasting and discharge prediction, and the performance of an LSTM model has been compared with other deep learning models. Although the tuning of hyperparameters, which influences the performance of an LSTM model, is necessary, no sufficient knowledge has been obtained. In this study, we tuned the hyperparameters of an LSTM model to investigate the influence on the model performance, and tried to obtain a more suitable hyperparameter combination for the imputation of missing discharge data of the Daihachiga River. A traditional method, linear regression with an accuracy of 0.903 in Nash–Sutcliffe Efficiency (NSE), was chosen as the comparison target of the accuracy. The results of most of the trainings that used the discharge data of both neighboring and estimation points had better accuracy than the regression. Imputation of 7 days of the missing period had a minimum value of 0.904 in NSE, and 1 day of the missing period had a lower quartile of 0.922 in NSE. Dropout value indicated a negative correlation with the accuracy. Setting dropout as 0 had the best accuracy, 0.917 in the lower quartile of NSE. When the missing period was 1 day and the number of hidden layers were more than 100, all the compared results had an accuracy of 0.907–0.959 in NSE. Consequently, the case, which used discharge data with backtracked time considering the missing period of 1 day and 7 days and discharge data of adjacent points as input data, indicated better accuracy than other input data combinations. Moreover, the following information is obtained for this LSTM model: 100 hidden layers are better, and dropout and recurrent dropout levels equaling 0 are also better. The obtained optimal combination of hyperparameters exceeded the accuracy of the traditional method of regression analysis.