A spatiotemporal estimation method for hourly rainfall based on F-SVD in the recommender system. (October 2021)

• Record Type: Journal Article
• Authors: Chen, Hua; Sheng, Sheng; Xu, Chong-Yu; Li, Zhiyu; Zhang, Wen; Wang, Shaowen; Guo, Shenglian
• Is Part Of: Environmental modelling & software. Volume 144(2021)
• Publication Date: 2021-10
• Subjects: Rainfall estimation -- Spatiotemporal interpolation -- Recommendation system -- Singular value decomposition -- Matrix factorization; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105148
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: A spatiotemporal estimation method based on F-SVD is proposed to estimate rainfall using gauge observation. F-SVD is utilized to decompose the spatiotemporal matrix consisted of rainfall data. F-SVD has higher accuracy and lower uncertainty compared to OK and IDW. Through combination with F-SVD, the accuracy of IDW and OK can be greatly improved. It is a practical method to process data for regional hydrological modelling.