| Application of BP artificial neural network on prediction of soil water content |
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| DOI：10.7606/j.issn.1000-7601.2005.05.19 |
| Key Words: soil moisture back-propagation neural network model forecast |
| Author Name | Affiliation | | ZHOU Liang-chen | Key Lab of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest A＆F University,Yangling,Shaanxi 712100,China | | KANG Shao-zhong | Key Lab of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest A＆F University,Yangling,Shaanxi 712100,China；The Reseach Center of Water Issuse of Beijing Agriculture University,Beijing 100036,China | | JIA Yun-mao | The Bureau of Upper and Middle Reaches of Yellow River,Xi’an,Shaanxi 710021,China |
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| Abstract: |
| The movement of soil moisture is influenced by multiple factors, such as weather elements, soil characteristics and crop growth, etc. Based on the observation data of soil water content and meteorological data during 1993 to 2001, a back-propagation (BP) neural network model for soil water content forecast is established. The application results show that the predicted values are coincident well with the observed values, and it is feasible to apply the model in predicting soil moisture condition and setting up irrigation plans. |
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