Mid- to long-term runoff prediction by combining the deep belief network and partial least-squares regression
| Autor: | Ping Ai, Chuansheng Xiong, Song Yanhong, Min Hong, Zhaoxin Yue |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Computer science 0207 environmental engineering 02 engineering and technology Geotechnical Engineering and Engineering Geology 01 natural sciences Term (time) Deep belief network Partial least squares regression Statistics 020701 environmental engineering Surface runoff 0105 earth and related environmental sciences Civil and Structural Engineering Water Science and Technology |
| Zdroj: | Journal of Hydroinformatics. 22:1283-1305 |
| ISSN: | 1465-1734 1464-7141 |
| Popis: | Data representation and prediction model design play an important role in mid- to long-term runoff prediction. However, it is challenging to extract key factors that accurately characterize the changes in the runoff of a river basin because of the complex nature of the runoff process. In addition, the low accuracy is another problem for mid- to long-term runoff prediction. With an aim to solve these problems, two improvements are proposed in this paper. First, the partial mutual information (PMI)-based approach was employed for estimating the importance of various factors. Second, a deep learning architecture was introduced by using the deep belief network (DBN) with partial least-squares regression (PLSR), together denoted as PDBN, for mid- to long-term runoff prediction, which solves the problem of parameter optimization for the DBN using PLSR. The novelty of the proposed method lies in the key factor selection and a novel forecasting method for mid- to long-term runoff. Experimental results demonstrated that the proposed method can significantly improve the effect of mid- to long-term runoff prediction. Also, compared with the results obtained by current state-of-the-art prediction methods, i.e., DBN, backpropagation neural networks, and support vector machine models, our prediction results demonstrate the performance of the proposed method. |
| Databáze: | OpenAIRE |
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