Abstract: It is reported that economic loss caused by natural disasters has been increasing in recent years in China,so some more accurate methodologies should be developed to predict and mitigate the disaster loss. Since agriculture in China is still one of the most vulnerable departments, the agricultural areas affected and injured by natural disasters are then chosen for modeling and analyzing. The nonlinear and unitary regression models are developed by using SAS software, for predicting agricultural areas affected and injured by natural disasters. As to the former, the affected area, four predicting models are established by using STEPAR,ARIMA, Time Delay Neural Network BPl and BP2 methods. Then, four integrated models are built up for diminishing accidental errors caused by each specific model, and in turn improving prediction precision. The affected area by natural disasters is first calculated by the four predicting models, respectively. Then the modeling results are nonlinearly averaged by the four integrated models to derive the final prediction results. The simulated results are quite consistent with recorded results from 1994 to 1999.Afterwards the model system is formally used for prediction in 2000.According to the model prediction, the affected area by natural disasters in 2000 is 5322.37×104 hm2.As to the injured area by natural disasters,a linear regression model with SAS REG process is developed for fitting the relationship between the affected and injured areas. The simulated results agree in general with recorded results from 1994 to 1999,but are not as well as the affected area predictions, which might indicate the linear regression model structure is not sufficient,and the data sets not long enough. The injured area in 2000,derived from the modeling results, is 2479.47×104 hm2.The prediction results of the model system are quite consistent with the observed ones in that year, which means that the model system,with certain improvements in future, can be used for agricultural disaster prediction and as reference to make disaster reduction decisions.