Artificial neural networks and adaptive neuro-fuzzy inference system in energy modeling of agricultural products

Nabavi‐Pelesaraei, Ashkan; Rafiee, Shahin; Hosseini-Fashami, Fatemeh; Chau, Kwok-wing

doi:10.1016/b978-0-12-817772-3.00011-2

Cited by 25 publications

(8 citation statements)

References 85 publications

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“…Data were collected through a face to face interview with 24 warm‐water fish producers with a questionnaire containing questions as pond area, the quantity of lime used for disinfection of ponds bed at the start of culture duration, the number and mean weights of different fingerlings, the quantity of inputs used for feed, electricity for aeration of water and watering ponds, the amounts of chemical fertilizers and farmyard manures mainly used for planktons growth, and fossil fuels required for weed control around the pond border, and fish harvesting. The questionnaire was structured based on the samples provided by the related references 20,21 . The validity of the research questionnaire and collected data was claimed by the local experts including the expert fishery researchers and related academic staffs from the study region.…”

Section: Methodsmentioning

confidence: 99%

Resource use efficiency of warm‐water fish culture upon different pond sizes

Firouzi

Kosari-Moghaddam

Radgoudarzi

2021

Env Prog and Sustain Energy

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Improving the energy efficiency of fish culture has always been a concern for warm‐water fish farming units. In this respect, the present study explored energy consumption flow and energy indices of warm‐water fish production in Guilan province, northern Iran. The impact of the energy equivalent of the system inputs on the energy equivalent of fish yield was modeled by the Cobb–Douglas function. The results revealed that total energy input, energy ratio, energy productivity, and energy intensity were 3710.4 MJ per 100 m2, 0.042, 0.009 kg MJ−1, and 109.77 MJ kg−1, respectively. Feed, electricity, and fossil fuel were the most energy‐intensive inputs accounting for 70.09%, 11.95%, and 11.70% of total energy use, respectively; representing the dominant role of feed in the energy input of warm‐water fish farming. Renewable and nonrenewable energy resources accounted for 6.97% and 93.03% of the total energy input of warm‐water fish culture system, respectively; requiring more care to cut the share of nonrenewable energy inputs. The Pareto chart determined that fingerling, labor, and electricity had the highest effects on the warm‐water fish yield. Therefore, more attention should be paid to the appropriate use of these inputs in warm‐water fish culture in the study region.

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Section: Methodsmentioning

confidence: 99%

Resource use efficiency of warm‐water fish culture upon different pond sizes

Firouzi

Kosari-Moghaddam

Radgoudarzi

2021

Env Prog and Sustain Energy

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“…In this agricultural study [25], the researchers considered diesel fuel, biocides, input costs and the cost of different operations in 75 wheat farms with modelling techniques including life cycle assessment (LCA), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods. Nabavi-Pelesaraei, et al [25] suggested using these methods, especially ANNs and ANFIS, to solve the optimization of energy consumption and estimate the yield of agriculture products without plant farming.…”

Section: Review Of Urban Waste Relationships Digital Platforms and Po...mentioning

confidence: 99%

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

2023

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Enhancing data-driven decision-making is vital for waste authorities. Although few studies have explored the influence of socio-economic indicators on waste tonnage, comprehensive analysis of urban waste data focusing on geographical information is also scarce. There is a dearth of dashboards for visualizing waste tonnage with spatial relationship maps. This study aims to present a prediction model useful for estimating urban waste by using personal income (I), the number of income earners (E), land values (L), the estimated resident population (P) and population density (D), called the IELPD measures. An innovative approach is developed to identify the correlation between urban household waste data and socio-economic factors and develop an advanced dashboard based on a geographic information system (GIS). To accomplish this, relationship maps and regression analysis are deployed to visualize household waste data spanning six years of waste production in New South Wales, Australia, classified into three categories: recyclable, residual and organic (RRO) wastes. Furthermore, this classification enables accessing the association between these three waste categories and the IELPD metrics. And there are four types of visualization generated, that is, thematic mapping, spatial relationship maps, correlation matrices and dashboard development. The regression analysis shows a substantial association between RRO waste tonnage, population changes and a minor correlation with land values. Overall, this study contributes to urban waste data storytelling and its spatiotemporal associations with socio-economic determinants. This paper offers a valuable prediction model of the IELPD metrics to estimate urban waste and visualize them in a dashboard allowing practitioners and decision-makers to track trends in the RRO waste stream in urban waste generally.

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“…Coefficients for calculation of energy consumption and cumulative exergy consumption in rice production (Yildizhan et al, 2020;Nabavi-Pelesaraei et al,…”

Section: Appendix Amentioning

confidence: 99%