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Dergiler / International Journal of Engineering and Geosciences / 2019 / Cilt: 4 - Sayı: 1

Comparison of different interpolation techniques in determining of agricultural soil index on land consolidation projects

Mevlüt UYAN
Sayfa
28–35
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Results show that all the methods present a good performance in the estimation with RMSE (root mean square error) and ME (mean error) close to 0%.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Land consolidation (LC) is a tool to improve the processing efficiency of agricultural area and the promotion of rural development same time an indispensable application for the promotion of sustainable agriculture.
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Temel Bulgular & Veri:The objective of this study was to evaluate and compare the performance of three interpolation methods for the agricultural SI values maps with GIS technology for LC projects.
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Sonuç & Katkı:Results show that all the methods present a good performance in the estimation with RMSE (root mean square error) and ME (mean error) close to 0%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Land consolidation (LC) · Soil index (SI) · Ordinary Kriging (OK)

Abstract

Land consolidation (LC) is a tool to improve the processing efficiency of agricultural area and the promotion of rural development same time an indispensable application for the promotion of sustainable agriculture. In order to achieve the reallocation process after LC, determining the correct of soil index (SI) for each of the agricultural parcels is very important for the success of LC projects. Nowadays, interpolation methods are extensively applied in the mapping processes to estimate the SI at unsampled sites. The objective of this study was to evaluate and compare the performance of three interpolation methods for the agricultural SI values maps with GIS technology for LC projects. The SI data were determined from 132 observation points. Three spatial interpolation methods Ordinary Kriging (OK), Inverse Distance Weighted (IDW), and Radial Basis Functions (RBFs) were utilized for modeling the agricultural SI values. The results indicated that all methods provided a high prediction accuracy of the mean concentration of SI. In this study, although the best performed interpolation method was the OK, the results showed that the performance differed slightly among three methods. Results show that all the methods present a good performance in the estimation with RMSE (root mean square error) and ME (mean error) close to 0%.

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Bu Makaleye Atıf Yap

UYAN, M. (2022). Comparison of different interpolation techniques in determining of agricultural soil index on land consolidation projects. International Journal of Engineering and Geosciences, Cilt: 4 - Sayı: 1, 28–35. https://ajindex.com/articles/540725

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Kaynakça (11)

  1. Boyd, J.P. (2015). Convergence and error theorems for Hermite function pseudo-RBFs: Interpolation on a finite interval by Gaussian-localized polynomials, Applied Numerical Mathematics 87, 125-144.
  2. Cafarelli, B., Castrignan, A., De Benedetto, D., Palumbo, A.D., Buttafuoco, G. (2015). A linear mixed effect (LME) model for soil water content estimation based on geophysical sensing: a comparison of an LME model and kriging with external drift, Environmental Earth Sciences 73(5), 1951-1960.
  3. Cui, Y. Q., Yoneda, M., Shimada, Y., Matsui, Y. (2016). Cost-Effective Strategy for the Investigation and Remediation of Polluted Soil Using Geostatistics and a Genetic Algorithm Approach, Journal of Environmental Protection 7(01), 99-115. de Amorim Borges, P., Franke, J., da Anunciação, Y. M. T., Weiss, H., Bernhofer, C. (2016). Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil, Theoretical and applied climatology 123(1-2), 335-348.
  4. Derlich, F. (2002). Land Consolidation: A Key for Sustainable Development – French Experience, FIG XXII International Congress, Washington, USA. Esen, Ö., Çay, T., Toklu, N. (2017). Evaluation of Land Reform Policies in Turkey, International Journal of Engineering and Geosciences 2(2), 61-67.
  5. Guo, B., Jin, X., Yang, X., Guan, X., Lin, Y., Zhou, Y. (2015). Determining the effects of land consolidation on the multifunctionlity of the cropland production system in China using a SPA-fuzzy assessment model, European Journal of Agronomy 63, 12–26.
  6. Jiang, G., Wang, W. (2017). Markov cross-validation for time series model evaluations, Information Sciences 375, 219-233.
  7. Kamali, M. I., Nazari, R., Faridhosseini, A., Ansari, H., Eslamian, S. (2015). The Determination of Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics, Water Resources Management 29(11), 3929-3940.
  8. Kuşçu Şimşek, Ç., Türk, T., Ödül, H., Çelik, M.N. (2018). Detection of Paragliding Fields by GIS, International Journal of Engineering and Geosciences 3(3), 119-125.
  9. Latruffe, L., Piet, L. (2014). Does land fragmentation affect farm performance? A case study from Brittany, France, Agricultural Systems 129, 68–80.
  10. Li, J., Heap, A.D. (2014). Spatial interpolation methods applied in the environmental sciences: A review, Environmental Modelling & Software 53, 173-189.
  11. Liu, Y., Hu, S., Sheng, D., Chang, L., Jia, M. (2016). Study of precipitation interpolation at Xiangjiang River Basin based on Geost