Intelligent Decision Model to Predict and Manage the Food Security Status of Dry Broad Beans

Dry Broad Beans (DBB) is the first strategic
legume crop in Egypt and other developing countries.
Also, it is considered one of the most popular Egyptian
foods from the Pharaonic age to now. This study aims to
develop an Intelligent Decision Model to Predict and
Manage the Food Security Status of DBB
(IDMPMFSSDBB). The proposed model utilizes Data
Mining Classification Technique (DMCT) and its
algorithms such as Random Tree (RT), Random Forest
(RF), ..., and J48 algorithms to classify and predict the
Food Security Status of DBB (FSSDBB) in agriculture
demographic regions in Egypt. It collects data features
which are Food Security Markers for DBB (FSMDBB)
from official statistical reports to build the Food Security
of DBB Dataset (FSDBBD). It determines the patterns of
DBB production and consumption to determine the
annual Average Per Capita of DBB (APCDBB), and the
Self-Sufficiency Ratio of DBB (SSRDBB) in the current
and future times according to the proposed model.IDMPMFSSDBB supports decision-makers with
informed decisions to meet the Egyptian population's
needs, supports the food security situation for DBB, and
fights grain instability prices, and crises in global trade
markets.It had
respectively the following pairs of Mean Absolute Errors
(MAE), and Root Mean Square Errors (RMSE): (0.024,
0.12), (0.049, 0.14), (0.042, 0.14), (0.027, 0.16), (0.037,
0.19), and (0.11, 0.28).