Rainfall is a key part of the hydrological cycle, and correct forecasting of rainfall is vital in the planning and management of water resources. Generalized regression neural network (GRNN) and support vector regression (SVR) were both applied to forecast monthly rainfall, and the conventional autoregressive model was built for comparison. Furthermore, Akaike Information Criteria were used to identify the proper inputs for the rainfall forecasting model. The data sets of monthly rainfall for a 53-year period from 1957 to 2010 in western Jilin Province, China, were used. The results indicated that the proper inputs would help in effectively improving the prediction accuracy. Furthermore, the results showed that both the SVR and the GRNN model performed better than the autoregressive model in forecasting monthly rainfall. SVR models outperformed all other models during the testing period in terms of the mean absolute error, root-mean-square error, coefficient of efficiency and R2. Therefore, SVR models were applied to forecast monthly rainfall for six cities including Baicheng, Qianguo, Fuyu, Qian'an, Changling and Tongyu.
Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China
Wenxi Lu, Haibo Chu, Zheng Zhang; Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China. Journal of Water Supply: Research and Technology-Aqua 1 February 2015; 64 (1): 95–104. doi: https://doi.org/10.2166/aqua.2014.002
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