Geomatics and Environmental Engineering, vol. 20, no. 4

Issue

Vol. 20 No. 4 (2026): Geomatics and Environmental Engineering

Date Log

Submitted
Nov 6, 2025
Accepted
Apr 7, 2026
Published
May 30, 2026
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

A Novel Housing Price Estimation Model Integrating GIS and DNN: A Case Study of Istanbul

https://doi.org/10.7494/geom.2026.20.4.33
Yakup Emre Çoruhlu
Karadeniz Technical University, Geomatics Engineering, Trabzon, Türkiye
https://orcid.org/0000-0002-8673-603X
Mustafa Dihkan
Karadeniz Technical University, Geomatics Engineering, Trabzon, Türkiye
https://orcid.org/0000-0002-0027-236X
Okan Yildiz
Karadeniz Technical University, Geomatics Engineering, Trabzon, Türkiye
https://orcid.org/0000-0002-7664-5361
Mehmet Özgür Çelik
Mersin University, Geomatics Engineering, Mersin, Türkiye
https://orcid.org/0000-0003-4569-888X
Hossein Azadi
University of Liège, Department of Geography, Liège, Belgium
https://orcid.org/0000-0003-4363-9405

Corresponding Author(s) : Mehmet Özgür Çelik

mozgurcelik@mersin.edu.tr

Geomatics and Environmental Engineering, Vol. 20 No. 4 (2026): Geomatics and Environmental Engineering

Abstract

Housing valuation is a concrete reflection of socio-economic inequalities in urban space. Particularly in densely populated, spatially fragmented large cities like Istanbul, the current official mass appraisal system used for property taxation fails to reflect market reality. This situation results in revenue losses in property taxes and spatial injustices. This study develops a Deep Neural Network (DNN)-based model that integrates spatially derived variables from Geographic Information Systems (GIS) and incorporates 24 objective variables related to location, structure, and access in Istanbul. The model, trained on
3,757 samples created using open-source big data, estimated housing values with high accuracy (R² = 0.979). The findings show that spatial differences in housing values are strongly related to urban variables such as accessibility and proximity to infrastructure. This approach not only produces housing value estimates but also provides a theoretical and methodological framework for spatial analyses of how value is produced in urban space. The study has the potential to support the development of a fair, transparent, and updatable mass appraisal system, especially for developing cities.

Keywords

house price estimation model mass appraisal property tax market value real estate DNN GIS
Çoruhlu, Y. E. ., Dihkan, M. ., Yildiz, O. ., Çelik, M. Özgür, & Azadi, H. . (2026). A Novel Housing Price Estimation Model Integrating GIS and DNN: A Case Study of Istanbul. Geomatics and Environmental Engineering, 20(4), 33–64. https://doi.org/10.7494/geom.2026.20.4.33
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Ding J., Cen W., Wu S., Chen Y., Qi J., Huang B., Du Z.: A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: A case study on house price in Wuhan. International Journal of Geographical Information Science, vol. 38(7), 2024, pp. 1315–1335. https://doi.org/10.1080/13658816.2024.2343771.
  2. Liu A., Jia M., Chen C.: From speculative dreams to ghost housing realities: Homeless homeowners and failed future-making in urban development. Urban Geography, vol. 46(8), 2025, pp. 1821–1841. https://doi.org/10.1080/02723638.2025.2456545.
  3. Yesmaganbetova A., Dautkanova D., Duisenbekova O., Ismatullayev S., Rsymbetov B., Serikbaeva G.: Quality of state real estate cadastre-maintenance process based on Ishikawa cause-and-effect diagram. Geomatics and Environmental Engineering, vol. 19(5), 2025, pp. 103–123. https://doi.org/10.7494/geom.2025.19.5.103.
  4. Klimach A.: Methodology for evaluating applicability of real estate information systems. Geomatics and Environmental Engineering, vol. 19(4), 2025, pp. 99–118. https://doi.org/10.7494/geom.2025.19.4.99.
  5. Bogin A., Doerner W., Larson W.: Local house price dynamics: New indices and stylized facts. Real Estate Economics, vol. 47(2), 2019, pp. 365–398. https://doi.org/10.1111/1540-6229.12233.
  6. Genc N., Colak H.E., Ozbilgin F.: Spatial performance approach to machine learning algorithms: A GIS-based comparison analysis for real estate valuation. Transactions in GIS, vol. 29(1), 2025, e13303. https://doi.org/10.1111/tgis.13303.
  7. Robin E.: Performing real estate value(s): Real estate developers, systems of expertise and the production of space. Geoforum, vol. 134, 2022, pp. 205–215. https://doi.org/10.1016/j.geoforum.2018.05.006.
  8. Ansenberg U.: Beyond displacement: The role of real-estate valuations in shaping urban displaceability. Environment and Planning C: Politics and Space, vol. 43(8), 2024, pp. 1483–1505. https://doi.org/10.1177/23996544241259310.
  9. El Yamani S., Hajji R., Billen R., Arroyo Ohori K., van der Vaart J., Hakim A., Stoter J.: Towards extending citygml for property valuation: Property valuation ADE. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. X-4/W5-2024, 2024, pp. 127–135. https://doi.org/10.5194/isprs-annals-X-4-W5-2024-127-2024.
  10. Cellmer R., Kobylińska K.: Housing price prediction – Machine learning and geostatistical methods. Real Estate Management and Valuation, vol. 33(1), 2025, pp. 1–10. https://doi.org/10.2478/remav-2025-0001.
  11. Leow K., Lindenthal T.: Enhancing real estate investment trust return forecasts using machine learning. Real Estate Economics, vol. 53, 2025, pp. 574–606. https://doi.org/10.1111/1540-6229.12527.
  12. Mete M.O., Yomralioglu T.: A hybrid approach for mass valuation of residential properties through geographic information systems and machine learning integration. Geographical Analysis, vol. 55(4), 2023, pp. 535–559. https://doi.org/10.1111/gean.12350.
  13. Reyes-Carranza M., Mbugua Muthama D.: Urban sprawl and the automation of building control in the peripheries of Nairobi. Urban Geography, vol. 46(6), 2025, pp. 1344–1360. https://doi.org/10.1080/02723638.2025.2450307.
  14. Shatkin G., Braswell T.H., Martinus M.: Mapping and the politics of informality in Jakarta. Urban Geography, vol. 44(5), 2023, pp. 939–963. https://doi.org/10.1080/02723638.2022.2059321.
  15. Wan W.X., Lindenthal T.: Testing machine learning systems in real estate. Real Estate Economics, vol. 51(3), 2023, pp. 754–778. https://doi.org/10.1111/1540-6229.12416.
  16. Peter N.J., Okagbue H.I., Obasi E.C., Akinola A.O.: Review on the application of artificial neural networks in real estate valuation. International Journal of Advanced Trends in Computer Science and Engineering, vol. 9(3), 2020, pp. 2918–2925. https://doi.org/10.30534/ijatcse/2020/66932020.
  17. Varma A., Sarma A., Doshi S., Nair R.: House price prediction using machine learning and neural networks, [in:] 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT 2018): 20–21 April 2018, Coimbatore, India, IEEE, 2018, pp. 1936–1939. https://doi.org/10.1109/ICICCT.2018.8473231.
  18. Aydinoglu A.C., Bovkir R., Colkesen I.: Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI. Survey Review, vol. 53(379), 2021, pp. 349–365. https://doi.org/10.1080/00396265.2020.1771967.
  19. Chen Y., Xue R., Zhang Y.: House price prediction based on machine learning and deeplearning methods, [in:] 2021 International Conference on Electronic Information Engineeringand Computer Science (EIECS2021): 23–26 September 2021, Changchun, China, IEEE, 2021, pp. 699–702. https://doi.org/10.1109/EIECS53707.2021.9587907.
  20. Chen K., Liu E., Deng M., Tan X., Wang J., Shi Y., Wang Z.: DKNN: deep kriging neural network for interpretable geospatial interpolation. International Journal of Geographical Information Science, vol. 38(8), 2024, pp. 1486–1530. https://doi.org/10.1080/13658816.2024.2347316.
  21. Wu S., Wang Z., Du Z., Huang B., Zhang F., Liu R.: Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science, vol. 35(3), 2021, pp. 582–608. https://doi.org/10.1080/13658816.2020.1775836.
  22. Yazdani M.: Machine learning, deep learning, and hedonic methods for real estate price prediction. arXiv, 2021, 2110.07151. https://doi.org/10.48550/arXiv.2110.07151.
  23. Khobragade A.N., Maheswari N., Sivagami M.: Analyzing the housing rate in a real estate informative system: A prediction analysis. International Journal of Civil Engineering and Technology, vol. 9(5), 2018, pp. 1156–1164.
  24. Contini C., Boncinelli F., Romano C., Scozzafava G., Casini L.: Price vector issue in a choice experiment: A methodological proposal. Food Quality and Preference, vol. 75, 2019. https://doi.org/10.1016/j.foodqual.2019.02.005.
  25. Jiang Z., Shen G.: Prediction of house price based on the back propagation neural network in the keras deep learning framework, [in:] 2019 6th International Conference on Systems and Informatics: ICSAI 2019: 2–4 November 2019, Vienna International Hotel, Shanghai, China, IEEE, 2019, pp. 1408–1412. https://doi.org/10.1109/ICSAI48974.2019.9010071.
  26. Pang X., Zhou Y., Wang P., Lin W., Chang V.: An innovative neural network approach for stock market prediction. The Journal of Supercomputing, vol. 76(3), 2020, pp. 2098–2118. https://doi.org/10.1007/s11227-017-2228-y.
  27. Tabar M.E., Başara A.C., Şişman Y.: Housing valuation study in Tokat province with multiple regression and artificial neural networks. Turkish Journal of Land Management, vol. 3(1), 2021, pp. 1–7. https://doi.org/10.51765/tayod.832227.
  28. Çağdaş V.: An application domain extension to CityGML for immovable property taxation: A Turkish case study. International Journal of Applied Earth Observation and Geoinformation, vol. 21, 2013, pp. 545–555. https://doi.org/10.1016/j.jag.2012.07.013.
  29. Poon J., Hoxley M., Fuchs W.: Real estate education: An investigation of multiple stakeholders. Property Management, vol. 29(5), 2011, pp. 468–487. https://doi.org/10.1108/02637471111178146.
  30. Coruhlu Y.E., Toludan T.: Data model development for ‘buying and selling’ transactions as a real estate acquisition method. Survey Review, vol. 52(374), 2020, pp. 403–414. https://doi.org/10.1080/00396265.2019.1626585
  31. Kocabıyık İ., Öktem R.: Multidimensional approaches in real estate valuation. Research of Financial Economic and Social Studies, vol. 6(1), 2021, pp. 160–177. https://doi.org/10.29106/fesa.874843.
  32. Demirel B., Yelek A., Alağaş H.M., Eren T.: Taşınmaz değerleme kriterlerinin belirlenmesi ve kriterlerin önem derecelerinin çok ölçütlü karar verme yöntemi ile hesaplanması [Determination of criteria for immovable valuation and calculation weights of criteria with multicriteria decision making method]. Kırıkkale University Journal of Social Sciences (KUJSS), vol. 8(2), 2018, pp. 665–682. https://izlik.org/JA82FP95LE.
  33. Thamarai M., Malarvizhi S.P.: House price prediction modeling using machine learning. International Journal of Information Engineering & Electronic Business, vol. 12(2), 2020, pp. 15–20. https://doi.org/10.5815/ijieeb.2020.02.03.
  34. Awoamim Y.J.B.D.G.: Vertical equity and uniformity in house price valuation, [in:] An Agenda for Repositioning African Property Market. Kaduna State University, Kafanchan Campus, Kaduna, Nigeria, African Real Estate Society, 2021, pp. 38–50.
  35. Batista P., Marques J.L.: Automated housing price valuation and spatial data, [in:] Gervasi O. et al. (eds.), Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part IV, Lecture Notes in Computer Science, vol. 12952, Springer, Cham 2021, pp. 366–381. https://doi.org/10.1007/978-3-030-86973-1_26.
  36. Selim H.: Determinants of house prices in Türkiye: Hedonic regression versus artificial neural network. Expert Systems with Applications, vol. 36(2), 2009, pp. 2843–2852. https://doi.org/10.1016/j.eswa.2008.01.044.
  37. Montero J.M., Mínguez R., Fernández-Avilés G.: Housing price prediction: parametric versus semi-parametric spatial hedonic models. Journal of Geographical Systems, vol. 20(1), 2018, pp. 27–55. https://doi.org/10.1007/s10109-017-0257-y.
  38. Limsombunchao V.: House price prediction: Hedonic price model vs. artificial neural network [paper presentation]. New Zealand Agricultural and Resource Economics Society 2004 Conference, June 25–26, 2004, Blenheim, New Zealand. https://doi.org/10.22004/ag.econ.97781.
  39. Truong Q., Nguyen M., Dang H., Mei B.: Housing price prediction via improved machine learning techniques. Procedia Computer Science, vol. 174, 2020, pp. 433–442. https://doi.org/10.1016/j.procs.2020.06.111.
  40. Phan T.D.: Housing price prediction using machine learning algorithms: the case of Melbourne city, Australia, [in:] 2018 International Conference on Machine Learning and Data Engineering (iCMLDE 2018): 3–7 December 2018, Sydney, Australia, IEEE, 2018, pp. 35–42. https://doi.org/10.1109/iCMLDE.2018.00017.
  41. Park B., Bae J.K.: Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Systems with Applications, vol. 42(6), 2015, pp. 2928–2934. https://doi.org/10.1016/j.eswa.2014.11.040
  42. Liu X.: Spatial and temporal dependence in house price prediction. The Journal of Real Estate Finance and Economics, vol. 47(2), 2013, pp. 341–369. https://doi.org/10.1007/s11146-011-9359-3.
  43. Yalpır S., Bünyan Ünel F.: Multiple Regression analysis for house valuation using GIS: A case study, [in:] Wang Y. (ed.), Electrical Engineering and Information Technology, WIT Transactions on Information and Communication Technologies, vol. 63, WIT Press, 2014, pp. 353–359.
  44. Yasar A., Sarıtas İ.: Use fuzzy logic approach for valuation of the house. [in:] Proceedings: The International Conference of Materials and Engineering Technology (TICMET’19), October 10–12, 2019, Gaziantep, Turkey, Gaziantep Universit, 2019, pp. 833–836.
  45. Cheung W., Guo L., Kawaguchi Y.: Automated valuation model for residential rental markets: Evidence from Japan. Journal of Spatial Econometrics, vol. 2(1), 2021, pp. 1–34. https://doi.org/10.1007/s43071-021-00009-0.
  46. Şişman A., Şişman Y.: Konutun değerine etki eden faktörlerin araştırılması [An investigation of factors affecting housing value], [in:] ISEAIA 2016: 4th International Symposium on Engineering, Artificial Intelligence and Applications: Proceedings: 2–4 November 2016, GAU – Girne, North Cyprus, Girne American University, 2016, pp. 230–236.
  47. Sarip A.G., Hafez M.B.: Fuzzy logic application for house price prediction. International Journal of Property Sciences, vol. 5(1), 2015, pp. 24–30. https://doi.org/10.22452/ijps.vol5no1.3.
  48. Alfiyatin A.N., Febrita R.E., Taufiq H., Mahmudy W.F.: Modeling house price prediction using regression analysis and particle swarm optimization. International Journal of Advanced Computer Science and Applications, vol. 8(10), 2017, pp. 323–326. https://doi.org/10.14569/IJACSA.2017.081041.
  49. Barbosa V., Suárez M., Cerda J., Thoene U.: Urban structure to determine equitable city growth for spatial justice: A case study of Chía-Bogotá, Colombia. Journal of Urban Mobility, vol. 5, 2024, 100080. https://doi.org/10.1016/j.urbmob.2024.100080.
  50. Weber L.: Maintaining Portland’s “progressive dystopia”: Housing, carcerality, and the real estate state. Urban Geography, vol. 45(9), 2024, pp. 1575–1595. https://doi.org/10.1080/02723638.2024.2333210.
  51. Banabak S., Kadi J., Schneider A.E.: Gentrification and the suburbanization of poverty: Evidence from a highly regulated housing system. Urban Geography, vol. 45(9), 2024, pp. 1596–1618. https://doi.org/10.1080/02723638.2024.2325197.
  52. Guerrero O.A., Law S.: The spatial structure of housing affordability and the impact of public infrastructure. Journal of Simulation, vol. 18(6), 2024, pp. 973–987. https://doi.org/10.1080/17477778.2024.2325428.
  53. Lamas-Abraira L., Colombo A., Oliva-Codina J., Martín-Oriol J.: Mapping unequal access to culture: A visual approach to culture-festival distribution and socio-economic asymmetries in Barcelona. Urban Geography, vol. 46(7), 2025, pp. 1642–1649. https://doi.org/10.1080/02723638.2025.2453351.
  54. Tuan N.T., Lan N.T.: The relationship between transport infrastructure and urban land prices: a study in Hanoi, Vietnam. SAGE Open, vol. 15(1), 2025, pp. 1–13. https://doi.org/10.1177/21582440241309409.
  55. Dökmeci V., Erdoğan N.: Spatial analysis of 2000–2018 residential prices in Istanbul. KAPU Trakya Journal of Architecture and Design, vol. 1(1), 2021, pp. 61–77.
  56. Shakespeare R.M.: Mapping the desirable and affordable city: Middle-income renter perceptions of housing opportunity. Urban Geography, vol. 46(6), 2025, pp. 1408–1431. https://doi.org/10.1080/02723638.2024.2436831.
  57. Taşan-Kok T.: Navigating the city: Role of property-market intelligence channels in urban governance networks. European Urban and Regional Studies, vol. 32(2), 2025, pp. 197–220. https://doi.org/10.1177/09697764241266411.
  58. Alexander P., Balavac-Orlic M., Lymer A., Mukherjee S.: Further improving our understanding of the tax awareness, tax literacy and tax morale of young adults. JOTA: Journal of Tax Administration, vol. 8(1), 2023, pp. 193–220. https://jota.website/jota/article/view/17.
  59. Demir O., Uzun B., Çete M.: Turkish cadastral system. Survey Review, vol. 40(307), 2008, pp. 54–66. https://doi.org/10.1179/003962608X253484.
  60. Aydoğdu M.: Ortaçağ Feodal Mülkiyet Anlayışı ve Osmanlı Hukukundaki Toprak Sisteminin Türk Hukukundaki Tarımsal İşletmelerin Mirasçılara Özgülenmesine Etkileri [The concept of medieval feudal ownership and influences of the land system in Ottoman Law on the transfer and allocation of agricultural enterprises to heirs]. Dokuz Eylul University Law Review, cilt. 17(2), 2015, pp. 1–26. https://izlik.org/JA87UC25NB.
  61. Çoruhlu Y.E.: Türk emlak vergi sisteminin irdelenmesi [Investigation of Turkish real estate tax system], [in:] İKSAD III – Uluslararasi Sosyal Bilimler Kongresi, Adana, Türkiye, 8–10 Mart 2019, cilt.1: Tam metin bildiri, 2019, pp. 427–443.
  62. Aliefendioğlu Y.: Emlak vergi değerlerinin takdirine ilişkin örgütlenme modelleri ve Türkiye için model önerisi üzerine tartışma [paper presentation, summary text]. Tapu ve Kadastro Modernizasyon Projesi Ek Finansmanı Gayrimenkul Değerleme Çalıştayı, February 27 – March 03, 2017, İzmir, Türkiye.
  63. Taşınmaz ticareti hakkında yönetmelik [Regulation on real estate trade]. Resmî Gazete, no. 30442, June 5, 2018. https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=24645&MevzuatTur=7&MevzuatTertip=5 [access: November 1, 2025].
  64. Uzun B., Yıldırım V., Çoruhlu Y.E., Yıldız O., Terzi F., Atasoy B.A.: The process of transition to a value-based distribution model in the Turkish land readjustment system. Land Use Policy, vol. 147, 2024, 107360. https://doi.org/10.1016/j.landusepol.2024.107360.
  65. Sahibinden. https://www.sahibinden.com/ [access: July 25, 2024].
  66. Cohen L., Manion M, Morrison K.: Research Methods in Education. Routledge, London 2000.
  67. Cohen J., Cohen P., West S.G., Aiken L.S.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Routledge, New York 2002.
  68. GDLRC [General Directorate of Land Registry and Cadastre]. https://parselsorgu.tkgm.gov.tr/#ara/cografi/40.91258663223653/39.017555326894886 [access: January 1, 2024].
  69. Gürsakal N.: Makine Öğrenmesi. Dora Yayıncılık, Bursa 2018.
  70. Lee C.: Enhancing the performance of a neural network with entity embeddings: An application to real estate valuation. Journal of Housing and the Built Environment, vol. 37(2), 2022, pp. 1057–1072. https://doi.org/10.1007/s10901-021-09885-2.
  71. Xu J.: A Novel deep neural network based method for house price prediction, [in:] 2021 International Conference of Social Computing and Digital Economy: ICSCDE 2021: Proceedings: 28–29 August 2021, Chongqing, China, IEEE, 2021, pp. 12–16. https://doi.org/10.1109/ICSCDE54196.2021.00012.
  72. Heaton J.B., Polson N.G., Witte J.H.: Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, vol. 33(1), 2017, pp. 3–12. https://doi.org/10.1002/asmb.2209.
  73. Yu P., Yan X.: Stock price prediction based on deep neural networks. Neural Computing and Applications, vol. 32(6), 2020, pp. 1609–1628. https://doi.org/10.1007/s00521-019-04212-x.
  74. Birman R., Segal Y., Hadar O.: Overview of research in the field of video compression using deep neural networks. Multimedia Tools and Applications, vol. 79(17–18), 2020, pp. 11699–11722. https://doi.org/10.1007/s11042-019-08572-3.
  75. Gulli A., Pal S.: Deep Learning with Keras: Implementing Deep Learning Models and Neural Networks with the Power of Python. Packt Publishing, Birmingham 2017.
  76. Moralı O., Yılmaz N.: Spatial heterogeneity in Istanbul housing market: A geographically weighted approach. Journal of Economics Finance and Accounting, vol. 7(4), 2020, pp. 298–307. https://doi.org/10.17261/Pressacademia.2020.1304.
  77. Gunduz L., Çakır M., Cepni O.: House price dynamics in Istanbul: District-level analysis of convergence clubs, spillovers, and key drivers. Borsa Istanbul Review, vol. 25(1), 2025, pp. 195–205. https://doi.org/10.1016/j.bir.2024.12.017.
  78. Tekin M., Sari I.U.: Real estate market price prediction model of Istanbul. Real Estate Management and Valuation, vol. 30(4), 2022, pp. 1–16. https://doi.org/10.2478/remav-2022-0025.
  79. Huang H., Yao X.A., Krisp J.M., Jiang B.: Analytics of location-based big data for smart cities: opportunities, challenges, and future directions. Computers, Environment and Urban Systems, vol. 90, 2021, 101712. https://doi.org/10.1016/j.compenvurbsys.2021.101712.
  80. Longley P.A.: Smart data infrastructure for urban analytics, city science, planning and design. Environment and Planning B: Urban Analytics and City Science, vol. 51(5), 2024, pp. 1041–1044. https://doi.org/10.1177/23998083241246327.
  81. Chen X., Biljecki F.: Mining real estate ads and property transactions for building and amenity data acquisition. Urban Informatics, vol. 1(1), 2022, 12. https://doi.org/10.1007/s44212-022-00012-2.
  82. Yu D., Fang C.: Urban remote sensing with spatial big data: a review and renewed perspective of urban studies in recent decades. Remote Sensing, vol. 15(5), 2023, 1307. https://doi.org/10.3390/rs15051307.
  83. Mostofi F., Toğan V., Başağa H.B.: Real-estate price prediction with deep neural network and principal component analysis. Organization, Technology and Management in Construction, vol. 14(1), 2022, pp. 2741–2759. https://doi.org/10.2478/otmcj-2022-0016.
  84. Wang Z., Wang Y., Wu S., Du Z.: House price valuation model based on geographically neural network weighted regression: The case study of Shenzhen, China. ISPRS International Journal of Geo-Information, vol. 11(8), 2022, 450. https://doi.org/10.3390/ijgi11080450.
  85. Gudivada V., Apon A., Ding J.: Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations. International Journal on Advances in Software, vol. 10(1–2), 2017, pp. 1–20.
  86. Gassar A.A.A., Cha S.H.: Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales. Applied Energy, vol. 291, 2021, 116817. https://doi.org/10.1016/j.apenergy.2021.116817.
  87. Jain A., Verma C., Kumar N., Raboaca M.S., Baliya J.N., Suciu G.: Image geosite estimation using convolutional auto-encoder and multi-label support vector machine. Information, vol. 14(1), 2023, 29. https://doi.org/10.3390/info14010029.
  88. Chicco D., Warrens M.J., Jurman G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, vol. 7, 2021, e623. https://doi.org/10.7717/peerj-cs.623.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 35 Download : 15

Downloads

Download data is not yet available.