Method based on the support vector machine and information diffusion for prediction intervals of granary airtightness

Abstract

Granaries should have good airtightness to reduce grain loss in storage. Prediction of granary airtightness at the design stage is beneficial in improving granary design. This paper proposes a method for the prediction interval (PI) of granary airtightness by using small sample data, which can guide designers with granary design. PI that the probability of the true target falling in it is markedly close or larger compared with the confidence level can be the decision basis of the granary design scheme. This study adopts support vector machine as the regression model trained by the airtightness data set of built granaries, and obtains the probability distribution of regression errors through information diffusion. The probability interval of errors is derived using a search algorithm, and PIs of granary airtightness can be acquired thereafter. Assessment indexes of PIs with confidence levels of 0.8 and 0.9 indicate that the proposed method can achieve confidence level and is superior to the comparative method using artificial neural network and bootstrap for PIs in cases of only a few samples. Thus, an innovative and feasible method is proposed for the computer-aided design of granary airtightness.