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Forecasting Monsoon Precipitation Using Artificial Neural Networks


doi: 10.1007/s00376-997-0014-0

  • This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.
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Manuscript History

Manuscript received: 10 September 2001
Manuscript revised: 10 September 2001
通讯作者: 陈斌, bchen63@163.com
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Forecasting Monsoon Precipitation Using Artificial Neural Networks

  • 1. School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,Chinese Academy of Meteorological Sciences, Beijing 100081

Abstract: This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

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