A spatiotemporal estimation method for hourly rainfall based on F-SVD in the recommender system

Hua Chen, Sheng Sheng, Chong Yu Xu, Zhiyu Li, Wen Zhang, Shaowen Wang, Shenglian Guo

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, a spatiotemporal estimation method based on Funk singular value decomposition (F-SVD) that considers the spatiotemporal correlation of rainfall is proposed to improve estimations from gauge observations. Hourly rainfall data of several flood events are selected to verify the proposed method by comparing with Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in Hanjiang basin, China. The results show that (1) F-SVD has the best performance in rainfall estimation, the larger the amount of rainfall event, the greater the improvement of F-SVD method as compared to OK and IDW; (2) through the combination/integration with F-SVD, the accuracy of IDW and OK can be greatly improved. Therefore, F-SVD can be employed as a practical method to estimate rainfall spatial distribution, which is essential data for regional hydrological modelling and water resource analysis.

Original languageEnglish (US)
Article number105148
JournalEnvironmental Modelling and Software
Volume144
DOIs
StatePublished - Oct 2021

Keywords

  • Matrix factorization
  • Rainfall estimation
  • Recommendation system
  • Singular value decomposition
  • Spatiotemporal interpolation

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

Fingerprint

Dive into the research topics of 'A spatiotemporal estimation method for hourly rainfall based on F-SVD in the recommender system'. Together they form a unique fingerprint.

Cite this