Modeling and Prediction of the Aesthetics of Urban Parks Based on Landscape Complexity Criterion

Document Type : Research Paper


1 College of Environment

2 BSc Student of Natural Resources - Environment, College of Environment, Karaj

3 Ph.D in Rangeland Management, College of Natural Resources, University of Tehran, Tehran, Iran


People are looking for new spaces that they can discover, and keep distance from dull and repetitive spaces, to create a place that is compatible with today's complexity and specific desires. The purpose of this study is to model the complexity of landscape in urban parks using artificial neural network in order to predict the complexity of landscape in urban parks and determination of the effect of different landscape variables on it. The present study was carried out in ten parks with an area of more than 10 hectares in Tehran city (Saei, Mellat, Nahj al-Balagheh, Iran Zaman, Laleh, Abo Atash, Taleghani, Jamshidieh, Gheitarieh and Niavaran) and in four municipality districts (1, 2, 3, and 6). The purpose of this study was to evaluate the complexity of landscape in urban park using a combination of user-based approach and artificial neural network modeling using 17 objective elements of landscape. According to the results, the model with 17-14-1 structure (17 input variables, 14 neurons in hidden layer and one output variable) according to the highest value of coefficient of determination in the three categories of training, validation and test datasets equal with 0.93, 0.85, and 0.87 created the best optimization performance. Accordingly, hard surfaces ratio, the mean of trees diameter and buildings, with the coefficients of 0.21, 0.15, and 0.12, respectively, are the most influential factors on the complexity of landscape in Tehran city parks, respectively. The presented model in this study is known as a decision support system in engineering design of urban parks and enables prediction of the complexity of landscape according to the environmental variables. .


Aghajani, H., Marvie Mohadjer, M.R., Jahani, A., Asef, M.R., Shirvany, A., Azaryan, M., 2014. Investigation of affective habitat factors affecting on abundance of wood macrofungi and sensitivity analysis using the artificial neural network. Iranian Journal of Forest and Poplar Research, 21(4), 9-19 (in Persian).
Boivin, M., Tanguay G.A., 2019. Analysis of the determinants of urban tourism attractiveness: The case of Quebec City and Bordeaux. Journal of Destination Marketing & Management, 11: 67–79.
Chhetri, P., Arrowsmith, C., 2008. GIS-based modelling of recreational potential of nature-based tourist destinations. Tourism Geographies, 10(2), 233–752.
Cracknell, D., White, M.P., Pahl, S., Depledge, M.H., 2016. A preliminary investigation into the restorative potential of public aquaria exhibits: a UK student-based study. Landsc. Res. 42 (1), 18–32.
Dupont, L., Ooms, K., Antrop, M., Van Eetvelde, V. 2016. Comparing saliency maps and eye-tracking focus maps: The potential use in visual impact assessment based on landscape photographs. Landscape and Urban Planning, 148, 17–26.
Güngör, S., Polat, T., 2018. Relationship between visual quality and landscape characteristics in urban parks. Journal of environmental protection and ecology.1: 939-948.
Jahani, A., 2016. Modeling of forest canopy density confusion in environmental assessment using artificial neural network. Journal of Forest and Poplar Research, 24(2), 310-322 (in Persian).
Jahani, A., 2017a. Sycamore failure hazard risk modeling in urban green space. Journal of spatial analysis environmental hazards, 3(4), 35-48 (in Persian).
Jahani, A., 2017b. Aesthetic quality evaluation modeling of forest landscape using artificial neural network, J. of Wood & Forest Science and Technology, 24(3):17-33 (in Persian).
Jahani, A., 2019. Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks. Journal of Forest Science, 65(2), 61-69
Jahani, A., Makhdoum, M., Feghhi, J., Etemad, V., 2011. Determining of landscape quality and look out points for ecotourism land use (Case study: Patom District of Kheyrud Forest). Journal of Environment Researches, 2(3): 13-20 (in Persian).
Jahani, A., Mohammadi Fazel, A., 2017. Aesthetic quality modeling of landscape in urban green space using artificial neural network. Journal of forest and wood products (JFWP) (Irainian Journal of natural resources), 69(4), 951-963 (in Persian).
Jahani, A., Saffariha, M., 2020. Aesthetic preference and mental restoration prediction in urban parks: An application of environmental modeling approach. Urban Forestry & Urban Greening, 54, 12-67.
Kalantary, S., Jahani, A., Jahani, R., 2020. MLR and Ann Approaches for prediction of Synthetic/natural Nanoibers Diameter in the Environmental and Medical Applications. Journal of Scientific Reports, 10, 1-15.
Kalantary, S., Jahani, A., Pourbabaki, R., Beigzadeh, Z., 2019. Application of ANN modeling techniques in the prediction of PCL/gelatin nanofibers diameter in the environmental and medical studies. RSC Advances 9: 24858-24874
Kerebel, A., Gélinas, N., Déry, S., Voigt, B., Munson, A., 2019. Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting. Journal of Landscape and Urban Planning. 185: 264.
Khaleghpanah, R., Jahani, A. Khorasani, N. Goshtasb, H., 2019.Prediction of citizens' satisfaction in urban parks using artificial neural network. Journal of Natural environment (79):2:239-250 (in Persian).
Marzi, R., 2017. The complexity of the urban landscape. Nation conference on Applied research in civil engineering, Architecture and urbanism, 1-13 (in Persian).
Nordh, H., Alalouch, C., Hartig, T., 2011. Assessing restorative components of small urban parks using conjoint methodology. Urban For. Urban Green. 10, 95–103.
Ribe, R. G., 2009. In-stand scenic beauty of variable retention harvests and mature forests in the U.S. Pacific Northwest: The effects of basal area, density, retention pattern and down wood.  Journal of Environmental Management, 91, 245–260.
Saeidi, S., Mohammadzadeh, M., Salmanmahiny, A., Mirkarimi, S.H., 2017. Performance evaluation of multiple methods for landscape aesthetic suitability mapping: a comparative study between Multi-Criteria Evaluation, Logistic Regression and MultiLayer Perceptron neural network. Land Use Policy 67, 1–12.
Saffariha, M., Jahani, A., Potter, D., 2020. Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach. BMC Ecology 20(48), 1-13.
Saffariha, M., Jahani, A., Jahani, R. Latif, S., 2021. Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks. Journal of Plant Methods. 17(10), 1-12.
Shams, S.R., Jahani, A.,Moinaddini, M., Khorasani, N., 2020. Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression. Modeling Earth Systems and Environment 6(3), 1467-1475.
Simensen, T., Halvorsen, R., Erikstad, L., 2018. Methods for landscape characterization and mapping: A systematic review. Land use policy 75, 557-569.
Soltani Fard, H., Masnavi, M., 2007. Complex landscape and landscape complexity, the role of complexity in sustainability of ecological systems. Journal of Environmental sciences, 4(2): 85-89 (in Persian).
Wang, R., Zhao, J., Michael, J., Meitner., Hu, Y., Xu, X., 2019. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery. Urban Forestry and Urban Greening, 41, 6-13.
Zheo, J., Xu, W., Li, R., 2017. Visual preference of trees: The effects attributes and seasons. 25, 19-25.