A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Rainfall Forecast Model Based on the TabNet Model
To further reduce the error rate of rainfall prediction, we used a new machine learning model for rainfall prediction and new feature engineering methods, and combined the satellite system’s method of observing rainfall with the machine learning prediction. Based on multivariate correlations among meteorological information, this study proposes a rainfall forecast model based on the Attentive Interpretable Tabular Learning neural network (TabNet). This study used self-supervised learning to help the TabNet model speed up convergence and maintain stability. We also used feature engineering methods to alleviate the uncertainty caused by seasonal changes in rainfall forecasts. The experiment used 5 years of meteorological data from 26 stations in the Beijing–Tianjin–Hebei region of China to verify the proposed rainfall forecast model. The comparative experiment proved that our proposed method improves the performance of the model, and that the basic model used is also superior to other traditional models. This research provides a high-performance method for rainfall prediction and provides a reference for similar data-mining tasks.
Rainfall Forecast Model Based on the TabNet Model
To further reduce the error rate of rainfall prediction, we used a new machine learning model for rainfall prediction and new feature engineering methods, and combined the satellite system’s method of observing rainfall with the machine learning prediction. Based on multivariate correlations among meteorological information, this study proposes a rainfall forecast model based on the Attentive Interpretable Tabular Learning neural network (TabNet). This study used self-supervised learning to help the TabNet model speed up convergence and maintain stability. We also used feature engineering methods to alleviate the uncertainty caused by seasonal changes in rainfall forecasts. The experiment used 5 years of meteorological data from 26 stations in the Beijing–Tianjin–Hebei region of China to verify the proposed rainfall forecast model. The comparative experiment proved that our proposed method improves the performance of the model, and that the basic model used is also superior to other traditional models. This research provides a high-performance method for rainfall prediction and provides a reference for similar data-mining tasks.
Rainfall Forecast Model Based on the TabNet Model
Jianzhuo Yan (author) / Tianyu Xu (author) / Yongchuan Yu (author) / Hongxia Xu (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A new model for predicting IRI using TabNet with hunter-prey optimization
| Taylor & Francis Verlag | 2024
An improved multifractal model for short-term rainfall forecast
| British Library Conference Proceedings | 2010
A Hybrid Grey Model to Forecast the Annual Maximum Daily Rainfall
| Springer Verlag | 2019
A Hybrid Grey Model to Forecast the Annual Maximum Daily Rainfall
| Online Contents | 2019