Early warning model of farming sustainability in counties based on improved artificial neural network

Vulnerable to the fluctuations of economy, resource and environment, the development of regional agricultural system may deviate from the normal orbit and result in a variety of warning conditions. In this paper, scientific early-warning model system for short-term county-scale sustainability of farming measure was established with the improved artificial neural network model as a core by combining skillfully with the “yellow” warning method and the traditional systematic approach. The BP neural network model was improved by the weighted principal component analysis method to optimize the initial weights of network. In the downstream region along Yellow River, two typical counties, Kenli and Fengqiu, were selected as the main study areas to complete the four key steps of early-warning. The results showed that, primarily, the county-scale early-warning model system for measure of farming sustainability based on the IANN method had good operability. Secondly, the improved BP algorithm can not only reflect the preferences of decision makers on the indicators, but also obtain a fast convergent and highly accurate neural network model. Finally, the empirical analysis of early-warning for the sustainability of farming measrue in the county scale achieved the desired goals and was in line with reality. The warning degrees in two counties from 2010 to 2014 were mainly light and moderate, on which abnormal fluctuations of warning signs from resource and environment subsystems, especially those having greater weights, had a more direct impact.