Predicting the Land Value using Regression Techniques and Artificial Neural Network
Velumani P1, Nampoothiri N2, Kavithra P3
1Velumani P, Department of Civil, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
2Nampoothiri N, Department of Civil, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
3Kavithra P, Department of Civil, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 431-436 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10211291S419/19©BEIESP | DOI: 10.35940/ijeat.A1021.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Land value can be an important factor which influences the cost of construction on working in the project. The land has socio-economic and environmental values and the confronted problems on land involves the increasing costs for developing the land such as built up, agricultural, residential, commercial and industrial areas. Hence this paper concentrates on prediction of land value by considering some important factors that affects it. The study area has been selected under Tirupur district, being a developing one in Tamil Nadu. The eleven areas in four different taluks under Tirupur district were chosen for research work. The average values of monthly variation are taken for the chosen factor for the years from 2001 to 2017. Using regression analysis and artificial neural network, the prediction has been done for the future land value. The performance of both the model executed good and fit for forecasting results. Though both the model showed better results, Artificial Neural Network (ANN) showed accuracy than regression method.
Keywords: Land value, Regression model, Artificial Neural Network, Google images, Historical Data.
Scope of the Article: Artificial Intelligence and Machine Learning