Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods

Yıl 2022, Cilt: 6 Sayı: 3, 199 - 205, 20.07.2022

Esra Aslı Çubukçu Vahdettin Demir Mehmet Faik Sevimli

https://doi.org/10.31127/tuje.889570

Cited By: 2

Öz

The digital elevation model (DEM) is the name given to a digital structure used to indicate the surface. Determination of features such as elevation, basin slope and basin area are very important in engineering applications. These properties are determined by the DEM and their power to represent accuracy or truth is vital in engineering applications. In addition to the latitude (X), longitude(Y) coordinate information, altitude information is required, and intermediate values are determined by different methods for DEM. In this study, Mert River Basin Samsun (Turkey) was chosen as the application area. Heights are estimated from X, Y coordinate information. Three different Artificial Neural Networks, IDW and Kriging methods were used. Artificial Neural Networks (ANN) were analyzed with three different inputs. These are: (i) x coordinate information; (ii) y coordinate information; (iii) It is in the form of x and y coordinate information and are used Radial Based Artificial Neural Network, Multilayer Artificial Neural Network and Generalized Artificial Neural Network. X and Y coordinate information was used in IDW and Kriging interpolation methods. Results were evaluated using Coefficient of Determination (R²), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as comparison criteria. According to the modeling results: It was observed that the results of all methods reached a sufficient level of accuracy. Kriging method was found to be the most successful model, followed by IDW and ANN. 

Anahtar Kelimeler

Digital Elevation Models, Artificial Neural Network, Samsun, Geostatistical methods, Mert River Basin

Kaynakça

• Akçın H, Kutoğlu H Ş & Terlemezoğlu B (2005). Deni̇z di̇bi̇ topoğrafyasının yapay si̇ni̇r ağlarıyla modellenmesi̇. TMMOB Harita ve Kadastro Mühendisleri Odası 10. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Turkey.
• Alp M & Cığızoğlu K (2004). Modeling the precipitation-flow relationship with different artificial neural network methods. İTÜ Engineering Journal, 3(1), 80–88.
• Arı A & Berberler M E (2017). Interface design for solving prediction and classification problems with artificial neural networks. Acta Infologica, 1(2), 55–73.
• Arslanoğlu M & Özçeli̇k M (2005). Improvement of numerical terrain elevation data. TMMOB Chamber of Surveying and Cadastre Engineers 10. Scientific and Technical Congress of Turkey, Ankara, Turkey.
• Çakır L & Yılmaz N (2014). Polynomials, radial basis functions and multilayer perceptron neural network methods in local geoid determination with GPS/levelling. Meas. J. Int. Meas. Confed., 57, 148–153.
• Çakır L (2015). Sayısal Yükseklik Modellerinde Klasik ve Esnek Hesaplama Yöntemlerinin Karşılaştırılması. TMMOB Harita ve Kadastro Mühendisleri Odası, 15. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Turkey.
• Demir V & Ülke Keskin A (2020). Height modeling with artificial neural networks (Samsun-Mert River Basin). Gazi Mühendislik Bilim. Dergisi, 6, 54–61.
• Demirkesen A C (2003). Sayısal yükseklik modellerinin analizi ve sel basman alanlarının belirlenmesi. TUJK 2003 Yılı Bilimsel Toplantısı, Konya, Turkey.
• Fang Y C & Wu B-W (2007). Neural network appication for thermal image recognition of low-resolution objects. J. Opt. A Pure Appl. Opt., 9(2), 134–144.
• Gemici E, Ardıçoğlu M & Kocabaş F (2013). Akarsularda debinin yapay zekâ yöntemleri ile modellenmesi. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Vol. 29, No. 2, pp 135–143.
• Gocic M & Trajkovic S (2013). Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Change, 100, 172–182.
• Güllü M, Yılmaz M, Yılmaz I & Turgut B (2011). Datum transformation by artificial neural networks for geographic information systems applications. International Symposium on Environmental Protection and Planning: Geographic Information Systems (GIS) and Remote Sensing (RS) Applications (ISEPP), İzmir, Turkey.
• Gümüş K & Şen A (2017). Sayisal Yükseklik Modellerinin doğruluğunu etkileyen faktörlerin varyans analizi ile istatiksel olarak incelenmesi, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 6(1), 46-58.
• Hani A F M, Sathyamoorthy D & Sagayan Asirvadam V (2011). Method for computation of surface roughness of digital elevation model terrains via multiscale analysis. Computers & Geosciences 37, 177–192.
• Karan O, Eraslan A & Kurnaz S (2004). Topografik bilgiler ve uydu görüntü verilerini kullanarak 3 boyutlu alan tanıma sistemi. Havacılık Ve Uzay Teknoloji Dergisi, 4, 31–40.
• Kesikoğlu H M, Çiçekli Y S & Kaynak T (2020). The identification of seasonal coastline changes from landsat 8 satellite data using artificial neural networks and k-nearest neighbor. Turkish Journal of Engineering (TUJE), 4(1), 47-56.
• Khosa F V, Feig G T, Van der Merwe M R, Mateyisi M J, Mudau A E & Savage M J (2019). Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem. Agric. For. Meteorol. l. 279, 1-20.
• Klingseisen B, Metternicht G & Paulus G (2008). Geomorphometric landscape analysis using a semi-automated gıs-approach. Environmental Modelling & Software, 23(1), 109–121.
• Konakoğlu B, Çakır L & Gökalp E (2016). 2D Coordinate transformation using artifıcial neural networks. ısprs - ınt. arch. photogramm. Remote Sens. Spat. Inf. Sci., 42, 183–186.
• Lei W & Qi X (2010). The application of BP neural network in GPS elevation fitting. In Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation; IEEE, 698–701.
• Niederheiser R, Rutzinger M, Bremer M & Wichmann V (2018). Dense ımage matching of terrestrial imagery for deriving high-resolution topographic properties of vegetation locations in alpine terrain. Int. J. Appl. Earth Obs. Geoinf., 66, 146–158.
• Okkan U & Dalkılıç H Y (2012). Radyal tabanlı yapay sinir ağları ile kemer barajı aylık akımlarının modellenmesi. İMO Teknik Dergi, 5957–5966.
• Özturk D & Kılıc F (2016). Geostatistical approach for spatial interpolation of meteorological data. Annals of the Brazilian Academy of Sciences, 88(4), 2121–2136.
• Öztürk A, Allahverdi N & Saday F (2022). Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering, 6(1), 54-62.
• Öztürk D, Şişman A, Şişman Y & Maraş E E (2010). Coğrafi bilgi sistemleri ile sayısal yükseklik modelinden topoğrafik ve morfolojik özelliklerin üretilmesi. VI. Ulusal Coğrafya Sempozyumu, 37-46, Ankara, Turkey.
• Papik K, Molnar B, Schaefer R, Dombovari Z, Tulassay Z, & Feher J (1998). Application of neural networks in medicine - A review. Med. Sci. Monit., 4(3), 538–546.
• Parlak A, İslamoğlu Y, Yaşar H & Eğrisöğüt A (2006). Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Appl. Therm. Eng., 26(8–9), 824–828.
• Partal T, Kahya E & Cığızoğlu K (2008). Yağış verilerinin yapay sinir ağları ve dalgacık dönüşümü yöntemleri ile tahmini. İTÜ Mühendislik Dergisi, 7(3), 73–85.
• Poggio T & Girosi F (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247(4945), 978–982.
• Schulmann T, Katurji M & Zawar-Reza P (2015). Seeing through shadow: Modelling surface irradiance for topographic correction of Landsat ETM+ data. ISPRS J. Photogramm. Remote Sens., 99, 14–24.
• Sürel A (2006). Genelleştirilmiş regresyon yapay sinir ağının su kaynakları mühendisliğinde kullanımı, Master Thesis, Istanbul Technical University, Istanbul, Turkey
• Şahin İ & Yakar M (2007). Accuracy Assessment of the Effect of Digital Elevation Models Generated from Different Sources on Orthophoto. 45–59.
• Taylan E D & Damçayırı D (2016). Isparta bölgesi yağış değerlerinin IDW ve Kriging enterpolasyon yöntemleri ile tahmini. Teknik Dergi, 27(3), 7551-7559.
• Tierra A, Dalazoana R & De Freitas S (2008). Using an Artificial Neural Network to Improve the Transformation of Coordinates between Classical Geodetic Reference Frames. Computers & Geoscience, 34, 181–189.
• Usul N & Paşaoğulları O (2003). Effect of scale and grid size for hydrological modeling. International Conference of GIS and Remote Sensing in Hydrology, Water Resources and Environment, 91-101.
• Wang P, Du J, Feng X & Kang G (2006). Effect of Uncertainty of Grid DEM on TOPMODEL: Evaluation and Analysis. Chinese Geographical Science, 16(4), 320–326.
• Yakar M (2008). Digital Elevation Model Generation By Robotic Total Station Instrument. Experimental Techniques, 33(2), 52 – 59.
• Yakar M, Yilmaz H M & Yurt K (2009). The Effect Of Grid Resolution In Defining Terrain Surface. Experimental Techniques 34 (6), 23-29.
• Yan L (2008). Based on the Triangular Grid Digital Elevation Model of the Terrain Modeling. World Academy of Science, Engineering and Technology, 4, 401–403.
• Yaprak S & Arslan E (2008). Kriging yönteminin geoit modellemesinde kullanılabilirliğinin araştırılması, İtü Dergisi, 7(5), 51-62.
• Krige D G (1951) A Statistical Approach to Some Mine Valuations and Allied Problems at The Witwatersrand. Master's thesis, University of Witwatersrand, Johannesburg, 272p
• Zhang X, Yu T & Zhao J (2020). Surface generation modeling of micro milling process with stochastic tool wear. Precision Engineering, 61, 170–181.