Modeling Energy Use and Economic Productivity of Different Fish Production Systems Using Artificial Neural Networks

Abstract

This study aimed to use artificial neural networks (ANNs) to predict output energy and economic indicators. So, data on two fish breeding sites were collected with a questionnaire, directly from the site owners and administrators and from the records. Then, the input and output energy of cold-water and hot-water fish were calculated. The cold-water fish were found to have a more favourable energy ratio (ER) (2.24), energy productivity (EP) (0.04 kg MJ⁻¹), specific energy (SE) (26.83 MJ kg^-1), and net energy gain (NEG) (33222.16 MJ kg^-1). According to the results, fish feed and electricity are two factors among energy consumption inputs whose proper management will increase energy efficiency. The benefit-to-cost ratio was positive for cold-water fish (1.54) and hot-water fish (2.45). The productivity of cold-water and hot-water fish was 0.58 kg $^-1 and 0.52 kg $^-1, respectively. The results of ANNs showed that R2 varied from 0.947 to 0.993 overall, from 0.912 to 0.964 for the training stage, and from 0.978 to 0.980 for the testing stage in the case of cold-water fish. Regarding hot-water fish, these values were 0.885-0.998, 0.923-0.952, and 0.952-0.995, respectively. So, ANNs can be used to predict output energy and economic productivity.