An Integrated Artificial Neural Network-based Precipitation Revision Model

• Tao Li,
• Wenduo Xu,
• Li Na Wang,
• Ningpeng Li,
• Yongjun Ren,
• Jinyue Xia,

Vol. 15, No. 5, May 31, 2021
10.3837/tiis.2021.05.006, Download Paper (Free):

• Precipitation prediction
• Machine Learning
• precipitation anomaly
• mean absolute error (MAE)
• time correlation coefficient (TCC)

Abstract

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today’s operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

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