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Ronny Berndtsson

Professor, Dep Director, MECW Dep Scientific Coordinator

Profile photo of Ronny Berndtsson

Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China

Author

  • Yiheng du
  • Ronny Berndtsson
  • Dong An
  • Linus Tielin Zhang
  • Feifei Yuan
  • Cintia B Uvo
  • Zhenchun Hao

Summary, in English

This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5° × 0.5°) together with climate indices. The model is based on principal component analyses (PCA) and artificial neural networks (ANN). Trend analyses show that mean annual and seasonal precipitation in the area is increasing depending on spatial location. For this reason, a multi-space model is especially suited for prediction purposes. The PCA-ANN model was examined using a 64-grid mesh over the source region of the Yangtze River (SRYR) and was compared to a traditional multiple regression model with a three-fold cross-validation method. Seasonal precipitation anomalies (1961–2015) were converted using PCA into principal components. Hierarchical lag relationships between principal components and each potential predictor were identified by Spearman rank correlation analyses. The performance was compared to observed precipitation and evaluated using mean absolute error, root mean squared error, and correlation coefficient. The proposed PCA-ANN model provides accurate seasonal precipitation prediction that is better than traditional regression techniques. The prediction results displayed good agreement with observations for all seasons with correlation coefficients in excess of 0.6 for all spatial locations.

Department/s

  • Division of Water Resources Engineering
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World

Publishing year

2019

Language

English

Publication/Series

Water

Volume

11

Issue

12

Document type

Journal article

Publisher

MDPI AG

Topic

  • Water Engineering

Keywords

  • Climate indices
  • Grid dataset
  • PCA-ANN
  • Precipitation prediction
  • The source region of the yangtze river

Status

Published

Project

  • Present and future precipitation variations in the source region of the Yangtze River, China

ISBN/ISSN/Other

  • ISSN: 2073-4441