Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

TL;DR

This study compares ANFIS and MLP machine learning models for predicting food production in Iran, demonstrating that ANFIS with Gbell membership functions yields the lowest prediction error, aiding policymakers in food security planning.

Contribution

The paper introduces and evaluates two machine learning models, ANFIS and MLP, for food production prediction, highlighting the superior performance of ANFIS with Gbell functions.

Findings

ANFIS with Gbell membership functions achieved the lowest prediction error.

Both models effectively predicted food production trends in Iran.

The models can assist policymakers in future food security planning.

Abstract

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.