بررسی عناصر و عوامل تاثیر گذار بر مسکن پایدار روستایی در پهنه کوهستانی(مطالعه موردی: سکونتگاه‌های روستایی شهرستان‌های ورزقان و هریس استان آذربایجان شرقی)

محورهای موضوعی : فصلنامه علمی برنامه ریزی منطقه ای

محمدرضا خاکزاد ¹ , بهرورز منصوری ² , حسن ستاری ساربانقلی ³

1 - دانشجوی دکتری معماری، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران.
2 - استادیار گروه معماری، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران.
3 - دانشیار گروه معماری و شهرسازی، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران .

تاریخ دریافت : 1399/09/22 تاریخ پذیرش : 1399/12/25 تاریخ انتشار : 1401/08/01

کلید واژه: "مسکن", "وستا", مسکن پایدار", "پهنه کوهستانی",

چکیده مقاله :

طراحی و بازسازی‌های سریع پس از حوادث طبیعی و تحولات سریع در مصالح و فن‌آوری ساخت، از عواملی است که در گسیختگی پدید آمده در پایداری محیط روستایی کشور نقش داشته است، هدف این تحقیق بررسی عناصر و عوامل تاثیر گذار بر مسکن پایدار روستایی در پهنه کوهستانی می باشد. روش تحقیق حاضر توصیفی- تحلیلی و از نوع پیمایشی می باشد. جامعه آماری تحقیق جمعیت 20 روستا معادل 6289 از دو شهرستان ورزقان و هریس می باشد. حجم نمونه شامل 322 نفر که از فرمول کوکران به دست آمد. جهت بررسی پایایی پرسشنامه، از آلفای کرونباخ استفاده شد. برای آزمون سوالات تحقیق، ابتدا نرمال بودن داده‌ها با استفاده از آزمون کولموگروف-اسمیرنوف مورد بررسی قرار گرفت و پس از تأیید نرمال بودن داده‌ها، اتحلیل عاملی تاییدی مرتبه دوم استفاده شد. محاسبات در نرم افزار SPSS و Amos انجام گرفت. یافته های شاخص نیکویی برازش (GFI) 915/0 است که نشان دهنده قابل قبول بودن این میزان برای برازش مطلوب مدل است. مقدار ریشه میانگین مربعات خطای برآورد (RMSEA) نیز 065/0 می‌باشد که با توجه به کوچکتر بودن از 08/0، قابل قبول بوده و نشان دهنده تأیید مدل پژوهش می‌باشد. همچنین شاخص توکر- لویس (TLI) 906/0؛ شاخص برازش تطبیقی (CFI) 903/0 و شاخص برازش مقتصد هنجار شده (PNFI) 71/0 است که همگی نشان دهنده برازش مطلوب و تأیید مدل پژوهش می‌باشد. نتایج حاصل نشان می‌دهد شاخص کالبدی بیشترین تاثیر در پایداری مسکن روستایی با بار عاملی 92/0 داشته است. شاخص اجتماعی کمترین تاثیر در پایداری مسکن روستایی با بار عاملی 81/0 می باشد.

چکیده انگلیسی:

Rapid design and reconstruction after natural disasters and rapid changes in materials and construction technology are among the factors that play a role in the disruption in the stability of the country's rural environment and also leads to the loss of rural architectural identity. And the factors affecting sustainable rural housing in the mountains. The present research method is descriptive-analytical and survey type. The statistical population of the study is the population of 20 villages equal to 6289 from Varzeqan and Harris counties. The sample size included 322 people obtained from Cochran's formula. The sampling method is simple random. Cronbach's alpha was used to evaluate the reliability of the questionnaire. To test the research questions, first the normality of the data was examined using the Kolmogorov-Smirnov test and after confirming the normality of the data, the second-order confirmatory factor analysis was used. Calculations were performed in SPSS and Amos software.
Based on the findings, the good fit index (GFI) is 0.915, which indicates the acceptability of this rate for optimal fit of the model. The root mean square of the estimation error (RMSEA) is 0.065, which is acceptable due to being smaller than 0.08 and indicates the confirmation of the research model. Also Tucker-Lewis index (TLI) 0.906; The adaptive fit index (CFI) is 0.903 and the normalized fit index (PNFI) is 0.71, all of which indicate the desired fit and approval of the research model. The results show that physical, environmental, economic and social factors in the region are effective in the sustainability of rural housing and among these factors; Physical index had the greatest impact on the stability of rural housing with a factor load of 0.92. The social index has the least impact on the sustainability of rural housing with a factor load of 0.81.

منابع و مأخذ:

• Ahadi, M., Sajadi, Zh., Yarigholi, V., (2019): Analysis and evaluation of livability indicators in urban areas Case study: 34 districts of Zanjan. Journal of Regional planning, 9(34), 131-148(In Persian).


• Ahadnejad, P., Khaledi, Sh., Ahmadi, M., (2020): Investigating the Long-term effect of dust on Health in order to prevent Its Impacts in Future Planning Case Study: Khuzestan Province. Journal of Regional planning, 10(39), 33-36(In Persian).


• Akbari, M.,& Samadzadegan, F., (2015): Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method. Int. J. Environ. Sci. Technol, 12, 3551–3562.


• Antanasijević, V. Pocajt, D. Povrenović, M. Ristić, A. Perić-Grujić., (2013): PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization , Science of the Total Environment , Vol. 443, pp. 511–519.


• Arhami, M., Kamali, N., Rajabi, M., (2013): Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environ Sci Pollut Res, 20, 4777–4789.


• Barrero, M.A., Grimalt, J.O., Canton, L., (2006): Prediction of daily ozone concentration maxima in the urban atmosphere. Chemom. Intell. Lab. Sys. 80, 67-76.


• Dunea, D., Pohoata, A., Iordache, S., (2015): Using wavelet–feedforward neural networks to improve air pollution forecasting in urban environments. Environ Monit Assess, 187(7),1-16.


• Durao, M., Mendes, T., Pereira, M., (2016): Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmospheric Pollution Research, 7, 961-970.


• Esmailnejad, M., Eskandari Sani, M., Borzaman, S., (2015): Evaluation and Zoning of Urban air Pollution in Tabriz. Journal of Regional planning, 5(19), 173-186(In Persian)


• Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J., (2015): Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118-128.


• Fernando, H. J. S., Mammarella, M. C., Grandoni, C., Fedele, P., Di Marco, R., Dimitrova, R., Hyde, P., (2012): Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environ. Pollut. 163, 62-67.


• Ghafouri Kesbi, F., Rahimi Mianji, G., Honarvar, M., Nejati Javaremi, A., (2016): Tuning and Application of Random Forest Algorithm in Genomic Evaluation. Research on Animal Production, 7(13), spring and Summer(In Persian).


• Grivas, G., & Chaloulakou, A., (2006): Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40, 1216 – 1229.


• Karatzas, K. D.,& Kaltsatos, S., (2007): Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simulation Modelling Practice and Theory, 15, 1310–1319.


• Lee, S., Ho, CH., Choi, YS, (2011): High-PM10 concentration episodes in Seoul, Korea: background sources and related meteorological conditions. Atmos Environ, 45(39), 7240–7247.


• Mohammadi, N., Khatibi, KH., Shaker khatibi, M,. Fatehi far,E., (2016): Predicting the concentration of gaseous pollutants in the air of Tabriz using a neural network. Civil and Environmental Engineering, 83(46) (In Persian).


• Moustris, K. P., Larissi, I. K., Nastos, P. T., Koukouletsos, K. V., Paliatsos, A. G., (2013): Development and Application of Artificial Neural Network Modeling in Forecasting PM10 Levels in a Mediterranean City. Water Air Soil Pollut, 224(8), 1634-1642.


• Noorani, V., Karimzadeh, H., Najafi, H., Hosseini, A., (2019): Predicting the concentration of NO2 and SO2 pollutants in the air of Tabriz using artificial neural network and adaptive neural-fuzzy inference system and comparing the obtained results. International Conference on civil engineering ,architectureand urban planning(In Persian).


• Osowski, S.,& louGaranty, K., (2007): Forecasting of the daily meteor ological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence, 20, 745 – 755.


• Perez, P., & Trier, A., (2011): Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile. Atmos. Environ., 35, 1783-1789.


• Sadr Mousavi,M.S., & Rahimi,A., (2010): Comparison of Multilayer Perceptron Neural Networks with multiple regression to predict the concentration of ozone in Tabriz, Natural Geography Research, Vol. 71, pp. 65-72(In Persian).


• Shamsoddini, A., Raval, S., Taplin, R., (2014): Spectroscopic analysis of soil metal contamination atound a derelictmine site in the blue mountains, australia”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-7, 2014 ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey.


• Shamsoddini, A., Trinder, J. C., Turner, R., (2015): Paired-data fusion for improved estimation of pine plantation structure. International Journal of Remote Sensing, 36, 1995-2009.


• Shamsoddini, A., Aboodi, M. R., Karami, J., (2017): Tehran air pollutants prediction based on random forest feature selection method. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W4, 2017 Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran.


• Sharma, M., Aggrawal, S., Bose, P., (2002): Meteorology – base forecasting of air quality index using neural network. International conference neural network, Singapoure, 374-378.


• Siwek, K., & Osowaski, S. S., (2012): Improving the accuracy of predict ion of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Engineering Applications of Artificial Intelligence, 25, 1246–1258.


• Tavakoli, M., & Esmaeili, A., (2014): Performance of ANN and fuzzy neural network adaptive for estimating of the concentration of suspended particles in the air of Tehran. Journal of Environmental Science and Engineering, 2, 75-84(In Persian).


• Wang, P., Liu, Y., Qin, Z., Zhang, G., (2015): A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Science of the Total Environment, 505, 1202–1212.


• Zhang, H., Zhang, W., Palazoglu, A., Sun, W., (2012): Prediction of ozone levels using a Hidden Markov Model HMM with Gamma distribution. Atmos Environ, 62, 64–73.