Machine Learning Models of Exergoenvironmental Damages and Emissions Social Cost for Mushroom Production
Abstract
Applying conventional methods for prediction of environmental impacts in agricultural production is not actually applicable because they usually ignore other aspects such as useful energy and economic consequence. As such, this article evaluates intelligent models for exergoenvironmental damage and emissions social cost (ESC) for mushroom production in Isfahan province, Iran, by three machine learning (ML) methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR). Accordingly, environmental life cycle damages, cumulative exergy demand, and ESC are examined by the ReCiPe2016 method for 100 tons of mushroom production after data collection by interview. Exergoenvironmental results reveal that, in human health and ecosystems, direct emissions, and resources and exergy categories, diesel fuel and compost are the main hotspots. Economic analysis also shows that total ESC is about 1035$. Results of ML models indicate that ANN with a 6-8-3 structure is the optimum topology for forecasting outputs. Moreover, a two-level structure of ANFIS has weak results for prediction in comparison with ANN. However, support vector regression (SVR) with an absolute average relative error (AARE) (%) between 0.85 and 1.03 (based on specific unit), a coefficient of determination (R2) between 0.989 and 0.993 (based on specific unit), and a root mean square error (RMSE) between 0.003 and 0.011 (based on specific unit) is selected as the best ML model. It is concluded that ML models can furnish comprehensive and applicable exergoenvironmental-economical assessment of agricultural products.