Prediction of elevation points using three different heuristic regression techniques

Year 2024, , 56 - 64, 19.01.2024

Vahdettin Demir , Ramazan Doğu

https://doi.org/10.31127/tuje.1257847

Cited By: 3

Abstract

The aim of this study is to estimate the digital elevation model, which is the most important data of the projects and needed in the engineering project, using latitude and longitude information of the elevation points and three different heuristic regression techniques. As the study area, an area with mid-level elevations, located in the Marmara region, and covering a part of the intersection of Edirne, Kırklareli and Tekirdağ provinces was chosen. In the study, the estimations were investigated for three different sized areas, and these areas are square areas with the dimensions of 1x1 km, 10x10 km and 100x100 km, respectively. A total of 3500 elevation points were used in the study, and this number is constant in all areas, and 60% of these points were used in the testing phase and 40% in the training phase. The models used in the study are M5 model tree (M5-tree), multivariate adaptive regression curves (MARS) and Least Square Support Vector Regression (LSSVR). The results of the models were evaluated according to three different comparison criteria. These, coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used. When the modeling results are examined; M5-Tree regression method gave the best results (1), LSSVR method was better than MARS methods (2), The most successful input data was found in datasets using X and Y coordinates information, and the worst results were found in datasets using X coordinates (3). As the study area increased, the model performance did not improve (4). The least error was obtained in the modeling of 1x1 km area, and the highest R² was obtained from the modeling of 10x10 km area (5). It was concluded that the M5-tree method is a very successful method in elevation modeling.

Keywords

M5-Tree, MARS, LSSVR, Elevation Point, Google Earth Pro

Project Number

1919B012107905

References

• Demir, V., & Ulke Keskin, A. (2020). Height Modelling with Artificial Neural Networks (Samsun_Mert River Basin). Gazi Journal of Engineering Sciences, 6(1), 54-61. https://dx.doi.org/10.30855/gmbd.2020.01.05
• Sahin, İ., Yakar, M. (2008). Farklı kaynaklardan elde edilen sayısal yükseklik modellerinin ortofoto doğruluğuna etkilerinin araştırılması. Harita Dergisi, 74(140), 45-59.
• Yakar, M. (2009). Digital elevation model generation by robotic total station instrument. Experimental Techniques, 33, 52-59. https://doi.org/10.1111/j.1747-1567.2008.00375.x
• Yakar, M., Yilmaz, H. M., & Yurt, K. (2010). The effect of grid resolution in defining terrain surface. Experimental Techniques, 34, 23-29. https://doi.org/10.1111/j.1747-1567.2009.00553.x
• Demir, V., & Çubukçu, E. A. (2021). Sezgisel Regresyon Teknikleri ile Sayısal Yükseklik Modellenmesi. Avrupa Bilim ve Teknoloji Dergisi, (24), 484-488. https://doi.org/10.31590/ejosat.916012
• Çakır, L. (2013) Sayısal Yükseklik Modellerinde Polinomlar ve Yapay Sinir Ağları Yöntemlerinin Karşılaştırılması. In Proceedings of the Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu (TUFUAB’2013), 23-25 Mayıs 2013, 1–4, Trabzon, Türkiye.
• Çakır, L. (2015) Sayısal Yükseklik Modellerinde Klasik ve Esnek Hesaplama Yöntemlerinin Karşılaştırılması. In Proceedings of the TMMOB Harita ve Kadastro Mühendisleri Odası, 15. Türkiye Harita Bilimsel ve Teknik Kurultayı, 25-­28 Mart 2015, 1-6, Ankara, Türkiye
• Konakoglu, B., Cakır, L., & Gökalp, E. (2016). 2D coordinate transformation using artificial neural networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 183-186. https://doi.org/10.5194/isprs-archives-XLII-2-W1-183-2016
• Biyik, M. Y., Atik, M. E., & Duran, Z. (2023). Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis. International Journal of Engineering and Geosciences, 8(2), 138-145. https://doi.org/10.26833/ijeg.1080624
• Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2023). Modeling of annual maximum flows with geographic data components and artificial neural networks. International Journal of Engineering and Geosciences, 8(2), 200-211. https://doi.org/10.26833/ijeg.1125412
• Demiryege, İ., & Ulukavak, M. (2022). Derin öğrenme tabanlı iyonosferik TEC tahmini. Geomatik, 7(2), 80-87. https://doi.org/10.29128/geomatik.870773
• Demirgül, T., Demir, V., & Sevimli, M. F. (2023). Model-Ağacı (M5-tree) yaklaşımı ile HELIOSAT tabanlı güneş radyasyonu tahmini. Geomatik, 8(2), 124-135. https://doi.org/10.29128/geomatik.1137687
• Kotan, B., & Erener, A. (2023). PM10, SO2 hava kirleticilerinin çoklu doğrusal regresyon ve yapay sinir ağları ile sezonsal tahmini. Geomatik, 8(2), 163-179. https://doi.org/10.29128/geomatik.1158565
• Tasdemir, S., & Ozkan, I. A. (2019). ANN approach for estimation of cow weight depending on photogrammetric body dimensions. International Journal of Engineering and Geosciences, 4(1), 36-44. https://doi.org/10.26833/ijeg.427531
• Uncuoglu, E., Citakoglu, H., Latifoglu, L., Bayram, S., Laman, M., Ilkentapar, M., & Oner, A. A. (2022). Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 129, 109623. https://doi.org/10.1016/j.asoc.2022.109623
• Bayram, S., & Çıtakoğlu, H. (2023). Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environmental Monitoring and Assessment, 195(1), 67. https://doi.org/10.1007/s10661-022-10662-z
• Zeybekoglu, U. (2018). Forecasting of Annual Mean Rainfall Using Artificial Neural Network and Wavelet Components: Case of Study Sinop Forecasting of Annual Mean Rainfall Using Artificial Neural Network and Wavelet Components: Case of Study Sinop. In Proceedings of the 1. International Technological Sciences and Design Symposium, 1700-1709, Giresun Türkiye.
• Hezarani, A. B., Zeybekoğlu, U., & Keskin, A. Ü. (2021). Hydrological and meteorological drought forecasting for the Yesilirmak river basin, Turkey. Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, 4(2), 121-135. https://doi.org/10.51764/smutgd.993792
• Öztürk, A., Allahverdi, N., & Saday, F. (2022). Application of artificial intelligence methods for bovine gender prediction. Turkish Journal of Engineering, 6(1), 54-62. https://doi.org/10.31127/tuje.807019
• Gülgün, O. D., & Hamza, E. R. O. L. (2020). Classification performance comparisons of deep learning models in pneumonia diagnosis using chest x-ray images. Turkish Journal of Engineering, 4(3), 129-141. https://doi.org/10.31127/tuje.652358
• Demir, V., & Doğu, R. (2022). Creating digital elevation model with Google Earth Pro. Advanced Engineering Days (AED), 4, 78-80.
• Hassan, O., Elnazeer, E., & Zomrawi, N. (2015). Application of Artificial Neural Network for Height Modelling. International Journal of Recent and Innovation Trends in Computing and Communication, 3(3), 1374-1377.
• Quinlan, J. R. (1992) Learning with Continuous Classes. Proceedings of Australian Joint Conference on Artificial Intelligence, Hobart 16-18 November 1992, 343-348.
• Mitchell, T. M. (1997). Machine learning. McGraw-Hill Science, ISBN: 0070428077
• Srivastava, R., Tiwari, A. N., & Giri, V. K. (2019). Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India. Heliyon, 5(10), e02692. https://doi.org/10.1016/j.heliyon.2019.e02692
• Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1-67. https://doi.org/10.1214/aos/1176347963
• De Andrés, J., Lorca, P., de Cos Juez, F. J., & Sánchez-Lasheras, F. (2011). Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Systems with Applications, 38(3), 1866-1875. https://doi.org/10.1016/j.eswa.2010.07.117
• Sharda, V. N., Patel, R. M., Prasher, S. O., Ojasvi, P. R., & Prakash, C. (2006). Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques. Agricultural water management, 83(3), 233-242. https://doi.org/10.1016/j.agwat.2006.01.003
• Yaseen, Z. M., Kisi, O., & Demir, V. (2016). Enhancing long-term streamflow forecasting and predicting using periodicity data component: application of artificial intelligence. Water resources management, 30, 4125-4151. https://doi.org/10.1007/s11269-016-1408-5
• Adnan, R. M., Petroselli, A., Heddam, S., Santos, C. A. G., & Kisi, O. (2021). Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards, 105, 2987-3011. https://doi.org/10.1007/s11069-020-04438-2
• Kisi, O., Parmar, K. S., Soni, K., & Demir, V. (2017). Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality, Atmosphere & Health, 10, 873-883. https://doi.org/10.1007/s11869-017-0477-9
• Bera, P., Prasher, S. O., Patel, R. M., Madani, A., Lacroix, R., Gaynor, J. D., ... & Kim, S. H. (2006). Application of MARS in simulating pesticide concentrations in soil. Transactions of the ASABE, 49(1), 297-307. https://doi.org/10.13031/2013.20228
• Sephton, P. (2001). Forecasting recessions: can we do better on MARS?. Federal Reserve Bank of St. Louis, 83, 39-49.
• Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9, 293-300. https://doi.org/10.1023/A:1018628609742
• Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2022). Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods. Turkish Journal of Engineering, 6(3), 199-205. https://doi.org/10.31127/tuje.889570