Method of Integral Assessment of Soil Quality in Rural-Urban Areas Based on the Fuzzy Logic

 
 
 
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    This article proposes a method of integral assessment of soil quality in rural-urban areas based on the fuzzy logic, both from the standpoint of the possibility of living in a given territory, and from the point of view of the possibility of keeping subsidiary farming. There are determined the linguistic variables describing the main soils components of rural-urban areas and terms describing the meaning of these variables. There are constructed the membership function that determine the ratio of the measured soil quality parameters to the terms and the rules of fuzzy inference. The authors developed an algorithm that describes this method.


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      [1] Benenson, I. Eds. M.F. Goodchild, Janelle D.G., 2004. Agent-Based Modeling: from Individual Residential Choice to Urban Residential Dynamics. Oxford: Oxford University Press .

      [2] Batty, M., 2007. Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. Cambridge: MIT Press.

      [3] Bulygin, S.Y.U., Bidolakh ,D.I. Lisetskiy,F.N.,2011. Assessment of soil humus content by processing their digital images. Scientific Bulletins of Belgorod State University, 15:.154-159 (In Russian).

      [4] Shekhar, S. Williamsn.,B., 2008. Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow. Transportation Research Record, 14

      [5] Kim, S., Keffeler ,M., Atkison ,T., Hainen, A. ,2017. Using Time Series Forecasting for Adaptive Traffic Signal Control. Proceedings of the 2017 International Conference on Data Mining:.34-39

      [6] Ivashchuk, O.A. Ivashchuk, O.D., 2013. Models of data mining in environmental information systems. Scientific Bulletins of Belgorod State University, 15 (158):.163-168 (In Russian).

      [7] Ivashchuk, O.A., Kvanin, D.A., 2014. Intellectual support of solutions in environmental safety managementю. Scientific review, 8: 619-626.

      [8] Ivashchuk, O. A., Konstantinov ,I.S., Lazarev, S.A., Fedoov ,V.I. ,2014. Research in the Field of Automated Environmental Safety Control for Industrial and Regional Clusters. International Journal of Applied Engineering Research, 9: 16813-16820.

      [9] Bakayeva, N.V., Shishkina, I.V., Matyushin, D.V., 2012. The model of ecologically safe motor transport infrastructure of municipal economy and the technique of integral assessment of its condition. Housing construction.,6: 78-81.

      [10] Plugotarenko, N.K., Varnavskiy, A.N.,2012. Primeneniye neyronnykh setey dlya postroyeniya modeli prognozirovaniya sostoyaniya gorodskoy vozdushnoy sredy. Engineering Don Bulletin, 23 (InRussian).

      [11] Nabil, I. , Sawalhi, E., 2012. Modeling the Parametric Construction Project Cost Estimate using Fuzzy Logic. International Journal of Emerging Technology and Advanced Engineering, .2 (4).

      [12] Ranzi,A., Lauriola,P., Marletto, V., Zinoni, F., 2003. Forecasting airborne pollen concentrations: Development of local models. Aerobiologia,19(1): 39–45.

      [13] Kalogirou, A., 2006. Artificial neural networks in energy applications in buildings. International Journal of Low-Carbon Technologies, 1(3): 201–216

      [14] Ours,A.S., Nassif, N., Long, D., 2016. Forecasting savings of building energy systems using artificial neural networks. Future Technologies Conference.


 

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Article ID: 22702
 
DOI: 10.14419/ijet.v7i4.36.22702




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