Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system

Khoshnevisan, Benyamin; Rafiee, Shahin; Omid, Mahmoud; Mousazadeh, Hossein; Clark, Sean

doi:10.1016/j.jclepro.2013.09.057

Cited by 162 publications

(46 citation statements)

References 38 publications

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“…All steps involved in the knowledge discovery process are essential, and model selection is indeed crucial (Khoshnevisan, Rafiee, Omid, Mousazadeh, & Clark, 2014).…”

Section: Model Building and Validationmentioning

confidence: 99%

“…ANN has successfully been employed for the solar radiation prediction and solar systems design that is essential for clean environment. Recently ANN is also used for predicting the environmental impacts by assessing the green house crops (Khoshnevisan et al, 2014;Khoshnevisan, Rafiee, Omid, Mousazadeh, & Sefeedpari, 2013). ANNs utilize neurons and simple processing units, combine data, and store relationships between independent and dependent variables.…”

Section: Predictive Data Mining Techniques and Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

Qazi

Fayaz

Wadi

et al. 2015

Journal of Cleaner Production

235

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“…All steps involved in the knowledge discovery process are essential, and model selection is indeed crucial (Khoshnevisan, Rafiee, Omid, Mousazadeh, & Clark, 2014).…”

Section: Model Building and Validationmentioning

confidence: 99%

Section: Predictive Data Mining Techniques and Artificial Neural Networkmentioning

confidence: 99%

The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

Qazi

Fayaz

Wadi

et al. 2015

Journal of Cleaner Production

235

View full text Add to dashboard Cite

“…Rule-based memories can be reduced using a nonlinear membership function, and hence the implementation cost can be reduced. Khoshnevisan, Rafiee, Omid, Mousazadeh, and Clark (2014) further opined that ANFIS can determine the proper size and optimal structure of the neural net, which reduces the time for generating the fuzzy rules and membership functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive neuro‐fuzzy inference system approach for urban sustainability assessment: A China case study

Tan

Shuai

Jiao

et al. 2018

Sustainable Development

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Urbanization, especially in developing countries, has led to numerous concerns, such as air pollution, traffic congestion and habitat destruction. Within this context, it is important to evaluate urban development as sustainable, and various sustainability assessment methods have been developed, including fuzzy logic approaches. However, predefined fuzzy rules and simple linear membership functions were used, which are largely based on the knowledge of subject experts. Therefore, this paper aims to introduce an adaptive neuro‐fuzzy inference systems (ANFIS) approach for urban sustainability assessment. With collected training samples from the Urban China Initiative, and the ANFIS approach was used to rank 185 selected cities in China. The results show that the ANFIS approach is appropriate for assessing urban sustainability, and the nonlinear membership functions fit the training samples better than the linear membership functions. Further discussion indicates that future research on sustainability assessment should be more integrated.

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“…Regional climate and crop selection have a direct relationship with the energy and water demand of the greenhouse system (Boulard et al., ; Dias et al., ; Khoshnevisan, Rafiee, Omid, Mousazadeh, & Clark, ; Ntinas et al., ). Ntinas (2017) examined seven scenarios of crop production, including five greenhouses designs with different heating systems and notes that heating demand is the primary hotspot for environmental burdens, followed by electricity, structures, and the use of fertilizers.…”

Section: Introductionmentioning

confidence: 99%

“…To assess the efficacy of OPV‐integrated greenhouses relative to conventional alternatives, environmental LCA and a detailed economic evaluation are required. While there have been several LCA studies that focus individually on the environmental impacts of greenhouses, greenhouse crop production, and OPVs (Anctil et al., ; Boulard et al., ; Dias et al., ; Khoshnevisan et al., ; Lizin et al., ; Ntinas et al., ; Yue et al., ), none have examined a regional comparison of both life cycle cost and environmental implications of OPV‐integrated greenhouses. Our study couples environmental and economic assessments to offer new insights into an emerging technology.…”

Section: Introductionmentioning

confidence: 99%

Environmental and economic impacts of solar‐powered integrated greenhouses

Hollingsworth

Ravishankar

O’Connor

et al. 2019

J of Industrial Ecology

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Greenhouse vegetable production plays a vital role in providing year‐round fresh vegetables to global markets, achieving higher yields, and using less water than open‐field systems, but at the expense of increased energy demand. This study examines the life cycle environmental and economic impacts of integrating semitransparent organic photovoltaics (OPVs) into greenhouse designs. We employ life cycle assessment to analyze six environmental impacts associated with producing greenhouse‐grown tomatoes in a Solar PoweRed INtegrated Greenhouse (SPRING) compared to conventional greenhouses with and without an adjacent solar photovoltaic array, across three distinct locations. The SPRING design produces significant reductions in environmental impacts, particularly in regions with high solar insolation and electricity‐intensive energy demands. For example, in Arizona, global warming potential values for a conventional, adjacent PV and SPRING greenhouse are found to be 3.71, 2.38, and 2.36 kg CO2 eq/kg tomato, respectively. Compared to a conventional greenhouse, the SPRING design may increase life cycle environmental burdens in colder regions because the shading effect of OPV increases heating demands. Our analysis shows that SPRING designs must maintain crop yields at levels similar to conventional greenhouses in order to be economically competitive. Assuming consistent crop yields, uncertainty analysis shows average net present cost of production across Arizona to be $3.43, $3.38, and $3.64 per kg of tomato for the conventional, adjacent PV and SPRING system, respectively.

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Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system

Cited by 162 publications

References 38 publications

The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

Adaptive neuro‐fuzzy inference system approach for urban sustainability assessment: A China case study

Environmental and economic impacts of solar‐powered integrated greenhouses

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