PubAg

Main content area

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production

Author:
Nabavi-Pelesaraei, Ashkan, Rafiee, Shahin, Mohtasebi, Seyed Saeid, Hosseinzadeh-Bandbafha, Homa, Chau, Kwok-wing
Source:
The Science of the total environment 2018 v.631-632 pp. 1279-1294
ISSN:
0048-9697
Subject:
acidification, algorithms, crop production, energy efficiency, environmental impact, eutrophication, fuzzy logic, global warming, life cycle assessment, neural networks, paddies, planning, prediction, production technology, Iran
Abstract:
Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg−1 and 66,112.94MJkg−1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.
Agid:
6299012