تحقیقات کاربردی علوم جغرافیایی

تحقیقات کاربردی علوم جغرافیایی

ارائه یک روش تخمین در پیش‌بینی شاخص‌های آسایش حرارتی انسان با بهره‌گیری از طبقه‌بندی ماشین عصبی باور عمیق

نویسندگان
علیرضا انتظاری
فاطمه میوانه
خسرو رضایی
فاطمه رحیمی
دانشگاه حکیم سبزواری
10.29252/jgs.18.51.23
چکیده
آسایش و عدم آسایش حرارتی انسان از طریق شاخص‌های تئوری و تجربی زیادی محاسبه می‌شوند که داده‌های ورودی این شاخص‌ها بسیاری از عناصر آب و هوایی از جمله سرعت باد، درجه حرارت، رطوبت، تابش خورشید و غیره می‌باشد. در مطالعه حاضر از داده‌های روزانه درجه حرارت، سرعت باد،رطوبت نسبی و ابرناکی شهر مشهد بین سال‌های1392-1383 استفاده شده است. ابتدا پارامتر Tmrt در محیط نرم افزار Ray Man محاسبه و با استفاده از نرم افزار Bioklima مقادیر شاخص‌های UTCI وPMV استخراج گردید. نتایج مطالعه نشان می‌دهد که شدیدترین تنش‌های سرمایی بر اساس شاخص PMV در فصل زمستان و اواخر فصل پاییز مشاهده می‌شود. و شاخص UTCI نیز در ماه‌های دی و بهمن بیشترین تنش سرمایی را دارند. با وجود توانایی شبکه‌های عصبی، پیش بینی عملکرد آینده شبکه (تعمیم یافتگی) به سادگی امکان‌پذیر نیست و لذا مدل جدیدی در این مقاله ارائه گردیده که از شبکه‌های عصبی مبتنی بر ماشین بولتزمان محدود شده یا شبکه‌های عصبی باور عمیق بهره گرفته است. با بکارگیری این ساختار، معیارهای میانگین مربعات خطای استاندارد (MSE) و میانگین مطلق درصد خطا (MAPE) محک خوردند و برای 7 شاخصه حاصل از داده‌های گردآوری شده که سه شاخصه مربوط به زمان وقوع شرایط آب و هوایی و سایرین، شاخص‌های حرارتی آسایش انسان است، در سیستم ارزیابی گردید. ارزیابی با تقسیم داده‌ها به بخش‌های آموزشی و آزمایشی و به ترتیب به نسبت‌های دوسوم، پنجاه درصد و یک سوم صورت پذیرفته است و دو محک MSE و MAPE محاسبه شدند. عملکرد سیستم پیشنهادی در پیش بینی وضعیت آسایش حرارتی انسان مطلوب بود.




#s3gt_translate_tooltip_mini { display: none !important; }
کلیدواژه‌ها
آسایش حرارتی
شبکه عصبی
شرایط آب و هوایی
سلامت انسان

عنوان مقاله English

An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief

نویسندگان English

Alireza Entezari
Fatemeh Mayvaneh
Khosro Rezaie
Fatemeh Rahimi
Assistant Professor of climatology, Hakim Sabzevari University, Sabzevar, Iran.
چکیده English

Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.

کلیدواژه‌ها English

thermal comfort
Neural Network
weather conditions
human health
Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43-47.
Arens, E. A., Gonzalez, R., & Berglund, L. (1986). Thermal comfort under an extended range of environmental conditions. ASHRAE Transactions, 92.
Aryal, S., & Gutierrez-Osuna, R. (2016). Data driven articulatory synthesis with deep neural networks. Computer Speech & Language, 36, 260-273.
Basu, R. (2009). High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ Health, 8(1), 40.
Blażejczyk, K. (2011). Assessment of regional bioclimatic contrasts in Poland. Miscellanea Geographica-Regional Studies on Development, 15, 79-91.
Błażejczyk, K. (2011). Mapping of UTCI in local scale (the case of Warsaw). Prace i Studia Geograficzne WGSR UW, 47, 275-283.
Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H., & Tinz, B. (2012). Comparison of UTCI to selected thermal indices. International Journal of Biometeorology, 56(3), 515-535.
Blazejczyk, K., Jendritzky, G., Bröde, P., Fiala, D., Havenith, G., Epstein, Y., . . . Kampmann, B. (2013). An introduction to the Universal Thermal Climate Index (UTCI).
Braga, A. L., Zanobetti, A., & Schwartz, J. (2002). The effect of weather on respiratory and cardiovascular deaths in 12 US cities. Environmental Health Perspectives, 110(9), 859.
Carder, M., McNamee, R., Beverland, I., Elton, R., Cohen, G., Boyd, J., & Agius, R. (2005). The lagged effect of cold temperature and wind chill on cardiorespiratory mortality in Scotland. Occupational and environmental medicine, 62(10), 702-710.
Chen, H.-S., Chen, C.-Y., Chang, C.-T., & Hsieh, T. (2014). The construction and application of a carrying capacity evaluation model in a national park. Stochastic environmental research and risk assessment, 28(6), 1333-1341.
Cheng, V., Ng, E., Chan, C., & Givoni, B. (2012). Outdoor thermal comfort study in a sub-tropical climate: a longitudinal study based in Hong Kong. International Journal of Biometeorology, 56(1), 43-56.
Fanger, P. O. (1970). Thermal comfort. Analysis and applications in environmental engineering. Thermal comfort. Analysis and applications in environmental engineering.
Farajzadeh Asl, M., Darand, M., & Faghihzadeh, S. (2009). Climatic factors associated with mortality in the population of Tehran. Journal of Planning and Space Planning, 14(2), 290-302.
Hampel, R., Breitner, S., Rückerl, R., Frampton, M. W., Koenig, W., Phipps, R. P., . . . Schneider, A. (2010). Air temperature and inflammatory and coagulation responses in men with coronary or pulmonary disease during the winter season. Occupational and environmental medicine, 67(6), 408-416.
Handbook, A. F. (2009). American society of heating, refrigerating and air-conditioning engineers. Inc.: Atlanta, GA, USA.
Hinton, G. E. (2012). A practical guide to training restricted boltzmann machines Neural networks: Tricks of the trade (pp. 599-619): Springer.
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—Why another thermal index? International Journal of Biometeorology, 56(3), 421-428.
Kántor, N., & Unger, J. (2011). The most problematic variable in the course of human-biometeorological comfort assessment—the mean radiant temperature. Central European Journal of Geosciences, 3(1), 90-100.
Kariminia, S., Sh Ahmad, S., & Norhati, I. (2013). Landscape attributes, microclimate and thermal comfort of an urban square in moderate and dry climate. Paper presented at the Advanced Materials Research.
Kim, H.-G., Choi, C.-Y., Woo, J.-W., Choi, Y., Kim, K., & Wu, D. D. (2011). Efficiency of the modal shift and environmental policy on the Korean railroad. Stochastic environmental research and risk assessment, 25(3), 305-322.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
Marmor, M. (1975). Heat wave mortality in New York City, 1949 to 1970. Archives of Environmental Health: An International Journal, 30(3), 130-136.
McGregor, G. R. (2012). Human biometeorology. Progress in Physical Geography, 36(1), 93-109.
Nastos, P. T., & Matzarakis, A. (2012). The effect of air temperature and human thermal indices on mortality in Athens, Greece. Theoretical and applied climatology, 108(3-4), 591-599.
Olesen, B. W. (1982). Thermal comfort. Technical review, 2, 3-37.
Omonijo, A. G., Adeofun, C. O., Oguntoke, O., & Matzarakis, A. (2013). Relevance of thermal environment to human health: a case study of Ondo State, Nigeria. Theoretical and applied climatology, 113(1-2), 205-212.
Parsons, K. (2014). Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort, and performance: Crc Press.
Rutkowski, L., Jaworski, M., Pietruczuk, L., & Duda, P. (2014). The CART decision tree for mining data streams. Information Sciences, 266, 1-15.
Santee, W. R., & Wallace, R. F. (2005). Comparison of weather service heat indices using a thermal model. Journal of Thermal Biology, 30(1), 65-72.
Steadman, R. G. (1979). The assessment of sultriness. Part II: effects of wind, extra radiation and barometric pressure on apparent temperature. Journal of Applied Meteorology, 18(7), 874-885.
Thomas, P. (2004). Under the Weather: How Weather and Climate Affect Our Health: Summersdale Publishers LTD-ROW.
Tseliou, A., Tsiros, I. X., Lykoudis, S., & Nikolopoulou, M. (2010). An evaluation of three biometeorological indices for human thermal comfort in urban outdoor areas under real climatic conditions. Building and Environment, 45(5), 1346-1352.
Valencia-Barrera, R., Comtois, P., & Fernández-González, D. (2002). Bioclimatic indices as a tool in pollen forecasting. International journal of biometeorology, 46(4), 171-175.
van Hoof, J., Kort, H., Hensen, J., Duijnstee, M., & Rutten, P. (2010). Thermal comfort and the integrated design of homes for older people with dementia. Building and Environment, 45(2), 358-370.
Yue, W., Fan, P., Wei, Y. D., & Qi, J. (2014). Economic development, urban expansion, and sustainable development in Shanghai. Stochastic environmental research and risk assessment, 28(4), 783-799.