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| Comparison between BP neural network and GA-BP crop water demand forecasting model |
| JIANG Xianqun, CHEN Wufen* |
| Pearl River Water Resources Commission of the Ministry of Water Resources, Guangzhou, Guangdong 510610, China |
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Abstract Forecasting crop water requirement is an important basis for formulating a reasonable irrigation system. In view of the deficiencies of BP neural network, genetic algorithm(GA)has the characteristics of strong global search capability, and a prediction model of crop water requirement based on GA-BP neural network was established. Taking the experimental crops of the Lamu Farm in Guangzhou as the object of research, the results show that the mean square error and certainty coefficient of the test set of crop water requirement prediction model based on BP neural network are 0.037 and 0.648 respectively. The square error and the certainty coefficient of the rest set of crop water demand forecasting model based on the GA-BP neural network are 0.013 and 0.882 resiectively. The GA-BP crop water demand forecasting model has a convergence rate, certainty coefficient and performance better than the BP crop water demand forecasting model.
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Received: 24 April 2018
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