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Open AccessArticle

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

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Department of Water Resources Engineering, Lund University, 22100 Lund, Sweden
2
Center for Middle Eastern Studies, Lund University, 22100 Lund, Sweden
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State Key Laboratory of Hydrology, Water Resources, and Hydraulic Engineering, Hohai University, Nanjing 210000, China
4
National Cooperative Innovation Center for Water Safety & Hydro-Science, Hohai University, Nanjing 210000, China
*
Authors to whom correspondence should be addressed.
Water 2019, 11(12), 2440; https://doi.org/10.3390/w11122440
Received: 25 October 2019 / Revised: 15 November 2019 / Accepted: 18 November 2019 / Published: 21 November 2019
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: grid dataset; precipitation prediction; climate indices; PCA-ANN; the source region of the Yangtze River grid dataset; precipitation prediction; climate indices; PCA-ANN; the source region of the Yangtze River
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MDPI and ACS Style

Du, Y.; Berndtsson, R.; An, D.; Zhang, L.; Yuan, F.; Uvo, C.B.; Hao, Z. Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China. Water 2019, 11, 2440.

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