A clustering model based on an evolutionary algorithm for better energy use in crop production

Khoshnevisan, Benyamin; Bolandnazar, Elham; Barak, Sasan; Shamshirband, Shahaboddin; Maghsoudlou, Hamidreza; Altameem, Torki; Gani, Abdullah

doi:10.1007/s00477-014-0972-6

Cited by 30 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surge in world population growth along with the dramatic rise in per capita consumption, caused by industrialization, increased living standards, and globalization, can be identified as the most important factors driving the increase in worldwide energy demand (Khoshnevisan et al, 2015;Rajaeifar et al, 2014). On the other hand, the raised public awareness of this increased energy demand and diminishing fossil resources, as well as the negative environmental pollutions resulted from the huge consumption of fossil origin fuels have pushed researchers to focus on renewable energy sources (Rajaeifar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Process performance and modelling of anaerobic digestion using source-sorted organic household waste

Khoshnevisan

Tsapekos

Alvarado-Morales

et al. 2018

Bioresource Technology

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Process performance and modelling of anaerobic digestion using source-sorted organic household waste

Khoshnevisan

Tsapekos

Alvarado-Morales

et al. 2018

Bioresource Technology

Self Cite

View full text Add to dashboard Cite

“…The three most significant variables were selected and applied to develop an ANFIS model. ANFIS is a robust tool favored by researchers for modeling (Al-Ghandoor and Samhouri 2009;Petković et al 2012a, b;Petković and Ćojbašić 2012), making predictions (Hosoz et al 2011;Gocić et al 2015b;Sivakumar and Balu 2010) and control in engineering systems (Kurnaz et al 2010;Ravi et al 2011;Khoshnevisan et al 2015;Petković et al 2012a, b;Tian and Collins 2005). ANFIS facilitates a fuzzy modeling procedure to gather data (Aldair and Wang 2011) and it can also be used to organize fuzzy inference systems using input/output data pairs.…”

Section: Introductionmentioning

confidence: 98%

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

et al. 2015

Self Cite

View full text Add to dashboard Cite

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.Resumen En este estudio se desarrolló un sistema de inferencia adaptativa de lógica difusa (ANFIS) para predecir la producción de papa en Irán. Se colectaron datos relacionados con el rendimiento de papa de 2010 a 2011 de 50 productores en Hamedan, Irán. La red ANFIS resultante tiene una capa de insumos con ocho neuronas y una capa de salidas con una única neurona (rendimiento de papa). Los insumos de energía fueron mano de obra, diésel, fertilizantes químicos y estiércol de animales de granja, químicos, maquinaria, agua y semilla. Se seleccionaron los insumos más significativos y de influencia de los ocho insumos iniciales, y se usó la red ANFIS para escoger los parámetros que tienen la mayor influencia en el rendimiento de papa. Se creó un nuevo modelo ANFIS después que se seleccionaron los tres parámetros de mayor influencia. Entonces se utilizó el nuevo modelo ANFIS para estimar rendimiento usando los tres insumos de energía. Después, los resultados del modelo ANFIS se compararon con los resultados de la técnica de regresión de vector de respaldo (SVR). Los resultados finales revelaron que ANFIS suministró predicciones más precisas y tuvo la capacidad de generalizar. El coeficiente de correlación de Pearson (r) para la predicción del rendimiento de papa por ANFIS fue 0.9999 en las fases de formación y de prueba, e el modelo SVR tuvo un coeficiente de correlación de 0.8484 en formación y 0.9984 en prueba.

show abstract

“…Also, GHG emissions are linked with the above processes of the sector. Across the entire world, agriculture and the related industries are consuming a significant part of fossil fuels and responsible for a major portion of GHG emissions (Khoshnevisan et al 2015). In China, the agricultural and agrochemical industries are consuming over 15 % of the national total fossil energy and emitting nearly 20 % of total GHG emissions (Huang et al 2015).…”

Section: A Systematic View On Agricultural Activities and Ghg Emissiomentioning

confidence: 99%

Adaptation strategies for mitigating agricultural GHG emissions under dual-level uncertainties with the consideration of global warming impacts

Yue

Cai

et al. 2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Agriculture plays a central role in maintaining food security and achieving sustainable development for human society. It is a challenge for the agricultural sector to mitigate greenhouse gas (GHG) emissions and maintain agricultural production. However, dual-level uncertainties exist in the processes of agricultural GHG accounting and emission reduction management. In this research, an integrated approach for identifying adaptation strategies in agricultural GHG emission reduction management was developed through incorporating life cycle analysis (LCA) of agricultural production into a general mathematical programming model. This approach strengthened the applicability of LCA and the comprehensiveness of programming models in generating agricultural adaptive actions under different GHG emission restriction targets. Also, dual-level uncertainties of LCA and adaptation management can be effectively addressed. A case study was proposed to illustrate application of the approach in Dalian City, China. The results indicated that farming patterns in eight districts would change significantly. The total area of maize fields would account for the primary proportion (i.e., 40-45 %) in its agricultural sector. Rice, peanut and cabbage fields would be the minor contributors in terms of GHG emissions. In addition to effective rainfall (i.e., [156, 259] mm/ha), a certain amount of water would be supplied for agricultural irrigation to maximize the city's agricultural yields. Compared with other agricultural crops, rice fields would need the largest amount of irrigation water (i.e., [153.72, 277.98] Mt) to meet the requirements of local government plans.

show abstract

A clustering model based on an evolutionary algorithm for better energy use in crop production

Cited by 30 publications

References 39 publications

Process performance and modelling of anaerobic digestion using source-sorted organic household waste

Process performance and modelling of anaerobic digestion using source-sorted organic household waste

Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

Adaptation strategies for mitigating agricultural GHG emissions under dual-level uncertainties with the consideration of global warming impacts

Contact Info

Product

Resources

About