Forecasting wheat production in Syria using artificial intelligence models - نعامة, مجد (Latakia University Journal -Biological Sciences Series, 2023)
The research aimed to compare between the regression model and the multi-layered neural network model in terms of the predictive ability of the wheat crop production in Syria using some statistical criteria such as the mean squares of the estimated model errors and the average of the differences between the real values and the expected values for each model, in addition to predicting the crop production during the period (2023-2030) The research relied on the statistics of the World Food and Agriculture Organization (FAO) for production data and cultivated area during the period (1990-2020) in addition to data on temperature and annual rainfall, where agricultural production was adopted as a dependent variable (year of production, cultivated area, average annual temperature, and average annual precipitation) as independent variables. The stepwise multiple regression method was used to estimate the regression model, and the multi-layer module (Perceptron MLP) was used using the statistical software (IBM SPSS 20) to build the neural network model and test its accuracy, where 23 years of data were used in the training phase (percentage). 74.2%, and 8 years for the probationary stage at (25.8%) The cultivated area occupied the largest relative importance in terms of influencing production according to the regression and neural network models. The research results also showed the superiority of the neural network model over the regression model in terms of predictive ability by using the standard of mean squares errors of the estimated model and the standard of average differences between the real values and the expected values, as the average square error was (0.32) using the neural network model compared to (3.49) using the regression model The average difference between the real and expected values using the neural network was (397128.1) versus (3186642) for the regression model.Keywords: human development index, evolution, its sub-indicators, Syria. Keywords: production, wheat, regression model, artificial intelligence, Syria.