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کاربرد روش‌های هوش مصنوعی در شبیه‌سازی دمای روزانه خاک در اقلیم‌های خشک و نیمه‌خشک | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 17، دوره 26، شماره 1، خرداد 1398، صفحه 201-213 اصل مقاله (1012.24 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2019.119337 | ||
| نویسندگان | ||
| فاطمه بهمنی1؛ حسین پیری صحراگرد* 2؛ جمشید پیری3 | ||
| 1دانش‌آموخته کارشناسی ارشد بیابان‌زدایی، گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، ایران | ||
| 2استادیار، گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، ایران | ||
| 3مربی، گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، ایران | ||
| چکیده | ||
| تخمین دمای خاک یکی از مسائل مهم در برنامه‌ریزی طرح‌های بیابان‌زدایی، مدیریت منابع آب و استقرار پوشش گیاهی در مناطق خشک است. هدف از این پژوهش، مقایسه دقّت روش‌های هوش مصنوعی در برآورد دمای روزانه خاک با استفاده از داده‌های هواشناسی (دمای حداقل و حداکثر روزانه، ساعات آفتابی و تبخیر از تشتک) در شهرهای زابل و شیراز و شناخت عوامل دارای تأثیر بیشتر بر دمای خاک بود. بدین‌منظور با استفاده از داده‌های سال 1393-1390، دمای روزانه خاک در اعماق 5، 10، 20، 30، 50 و 100 سانتی‌متری با روش‌های شبکه عصبی‌مصنوعی، سیستم استنتاج عصبی- فازی تطبیقی، برنامه‌ریزی ژنتیک و روش ترکیبی شبکه عصبی- ژنتیک مدل‌سازی شد. نتایج حاصل با استفاده از معیارهای ریشه میانگین مربعات خطا و میانگین انحراف خطا و ضریب تعیین ارزیابی گردید. بر اساس نتایج، بین دمای هوا با دمای خاک در عمق‌های سطحی خاک وابستگی بیشتری وجود داشت، به‌طوری‌که بیشترین و کمترین میزان همبستگی بین مقادیر واقعی و مقادیر برآوردشده در عمق‌های 5 سانتی‌متری (میانگین 92/0R2=) و 100 سانتی‌متری (میانگین 56/0R2=) مشاهده شد. همچنین دقّت روش‌های مورد استفاده در برآورد دمای روزانه خاک در ایستگاه‌های مورد بررسی متفاوت بود. براساس نتایج، در ایستگاه زابل الگوریتم ترکیبی شبکه عصبی- ژنتیک و در ایستگاه شیراز مدل شبکه عصبی مصنوعی برآورد دقیق‌تری را از دمای خاک ارائه دادند (میانگین RMSE به‌ترتیب 69/3 و 86/2؛ میانگین MAE به‌ترتیب23/3 و 57/2). با توجه به نتایج این پژوهش پیشنهاد می‌گردد به‌منظور انتخاب زمان و عمق مناسب کاشت بذر در فعالیت‌های مرتبط با احیای پوشش گیاهی در مناطق خشک، با ملاحظه شرایط اقلیمی هر منطقه، از روش‌های هوش مصنوعی دقیق‌تر برای برآورد دمای خاک استفاده گردد. | ||
| کلیدواژه‌ها | ||
| بیابان‌زدایی؛ دمای خاک؛ داده‌های هواشناسی؛ هوش مصنوعی؛ شیراز؛ زابل | ||
| عنوان مقاله [English] | ||
| Application of artificial intelligence methaods to estimate soil daily temperature in arid and semi-arid climates | ||
| نویسندگان [English] | ||
| Fatemeh Bahmani1؛ Hosin Pirisahragard2؛ Jamshid Piri3 | ||
| 1Former M.Sc. Student in Combat Desertification, Department of Range and Watershed Management, Faculty of Soil and Water, University of Zabol, Iran | ||
| 2Assistant Professor, Department of Range and Watershed Management, Faculty of Soil and Water,University of Zabol, Iran | ||
| 3Faculty Member, Department of Water engineering, Faculty of Soil and Water, University of Zabol, Iran | ||
| چکیده [English] | ||
| Estimation of the soil temperature in arid regions is one of the most important issues in planning the projects of desertification, water resource management, and establishment of vegetation.The present study aimed to compare the accuracy of artificial intelligence methods in order to estimate soil daily temperatures using meteorological data (daily minimum and maximum of temperatures, sunshine, and evaporation), as well as, identifying the most important factors on soil temperature in Zabol and Shiraz synoptic stations. For this purpose, soil daily temperature was estimated at 5, 10, 20, 30, 50 and 100 cm depths by using the three-year period data (2011-2014) and artificial neural network, neuro-fuzzy adaptive genetic programming, and combined neural network-genetic algorithm approaches. Thae results were evaluated using the root mean square error (RMSE) and mean absolute error (MAE) and determination coefficient. Based on the results, there was more dependence between the air temperature and soil temperature at the topsoil so that, the highest and the lowest correlation between actual and simulated data were observed at 5 cm (mean R2=0.92) and 100 cm depths (mean R2=0.56), respectively. The accuracy of the methods used was different from each other in estimating the soil daily temperature. Based on results, in Zabol and Shiraz stations, combined neural networks - genetic algorithm approach and artificial neural network methods provided the most accurate estimation of soil daily temperature, respectively (the mean RMSE=3.69, 2.86 and mean MAE=3.23, 2.57 respectively). According to the results of the present study, it is suggested that in order to choose appropriate time and depth of seeding in the vegetation reclamation projects in arid regions, through considering of climatic conditions of each region, precise artificial intelligence techniques could be used to estimate the soil daily temperature. | ||
| کلیدواژه‌ها [English] | ||
| Desertification, soil temperature, meteorological data, artificial intelligence, Shiraz, Zabol | ||
| مراجع | ||
|
- Ahmed, S. and Simonovic, S. P., 2005. An artificial neural network model for generating hydrograph from hydro meteorological parameters. Journal of Hydrology, 315: 236-251. - Arvin, A. and Shojaeezadeh, K., 2014. Assessment of climate tourism in Shiraz city using physiologic equivalence temperature and predicted mean vote indexes. Journal of Physical Geography, 7 (26): 87-98. - Bazartseren, B., Hildebrand, G. and Holz, K., 2003. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing, 55: 439-450. - Behyar, M. B. and Kamali, G., 2007. Relationship between air temperature and surface and depth temperature. Geographical research, 23 (3): 81-102. - Bilgili, M., 2010. Prediction of soil temperature using regression and artificial neural network models. Meteorology and Atmospheric Physics, 110: 59-70. - Bilgili, M., Sahin, B. and Sangun, L., 2013. Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Environmental Monitoring and Assessment, 185: 347-358. - Bond-Lamberty, B., Wang, C. and Gower, S. T., 2005. Spatiotemporal measurement and modeling of stand-level boreal forest soil temperatures. Journal of Agricultural and Forest Meteorology, 131: 27-40. - Braley, W. A. and Zarling, J. P., 1991. MUT1D: user-friendly one-dimensional thermal model. In cold regions engineering, US army cold regions research and engineering laboratory, Hanover, NH, Sodhi DC (ed). American Society of Civil Engineers: New York, 1-10. - Haghverdi, A., Ghahraman, B., Joleini, M., Khoshnud Yazdi, A. A. and Arabi, Z., 2011. Comparison of different Artificial Intelligence methods in modeling water retention curve (Case study: north and northeast of Iran). Journal of Water and Soil Conservation, 18 (2): 65-84. - Hosseinzadeh Talaee, P., 2014. Daily soil temperature modeling using neuro-fuzzy approach. Theoretical and Applied Climatology, 118: 481-489. - Hu, Q.S. and Feng, S., 2002. A daily soil temperature dataset and soil temperature climatology of the contiguous United States. Climate and Bio-atmospheric Sciences Group, School of Natural Resource Sciences, University of Nebraska-Lincoln: Lincoln, Nebraska, 42: 1139-1156. - Jackson, T., Mansfield, K., Saafi, M., Colman, T. and Romine, P., 2008. Measuring soil temperature and moisture using wireless MEMS sensors. Journal of Measurement, 41: 381-390. - Jafari Golstani, M., Raeini Sarjaz, M. and Tabarahmadi, M., 2007. Estimation of soil depth temperature using curve analyzing and correlation of regression methods for Sari city. Journal of Agricultural and Natural Resources, 1(5): 112-123. - Jamalizadeh Tajabadi, M. R., Moghaddamnia, A. R., Piri, J. and Ekhtesasi, M. R., 2010. Application of artificial neural networks in dust storm prediction (Case study: Zabol city). Iranian Journal of Range and Desert Research, 17 (2): 205-220. - Karamouz, M., Tabesh, M., Nazif, S. and Moridi, A., 2005. Estimation of hydraulic pressure in water networks using artificial neural networks and fuzzy logic. Bimonthly Journal of Water and Wastewater, 53 (1): 3-14. - Kim, S. and Singh, V. P., 2014. Modeling daily soil temperature using data-driven models and spatial distribution. Theoretical and Applied Climatology, 118: 465-479. - Kisi, O., Tombul, M. and Kermani, M. Z., 2014. Modeling soil temperatures in different depths by using three different neural computing techniques. Theoretical and Applied Climatology, 121: 377-387. - Kumar, P., Kumar, D., Jaipaul, A. and Tiwari, K., 2012. Evaporation estimation using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference system techniques. Pakistan Journal of Meteorology, 8 (16): 81-88. - Maier, H. R. and Dandy, G. C., 1996. The use of artificial neural network for the prediction of water quality parameters. Journal of Water Research, 32 (4): 1013-1022. - Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S. and Han, D., 2009. Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques. Journal of Advances in Water Resources, 32: 89-97. - Najafi-mood, M. H., Alizadeh, A., Mohamadian, A. and Mousavi, J., 2008. Investigation of relationship between air and soil temperature at different depths and estimation of the freezing depth (Case study: Khorasan Razavi). Journal of Water and Soil (Agricultural Sciences and Technology), 22 (2): 456-466. - Napagoda, N. A. D. N. and Tiakaratne, C. D., 2012. Artificial Neural Network approach for modeling of soil temperature: A case study for Bathalagoda Area, Sri Lankan. Journal of Applied Statistics, 13: 39-59. - Ozturk, M., Salman, O. and Koc, M., 2011. Artificial neural network model for estimating the soil temperature. Canadian Journal of Soil Science, 91: 551-562. - Parsafar, N. and Maroufi, S., 2011. Estimation of soil temperature from air temperature using regression models, Artificial Neural Network and Adaptive Neuro-Fuzzy Inference system (Case study: Kermanshah region). Water and Soil Science, 21(3): 139-152. - Pierce, R. S., Lull, H. W. and Storey, H. C., 1958. Influence of land use and forest conditition on soil freezing and snow depth. Forestry Science, 4: 246-263. - Piri, J. and Kisi, O., 2015. Modelling solar radiation reached to the earth using ANFIS, NNARX, and empirical models (Case studies: Zahedan and Bojnurd stations). Journal of Atmospheric and Solar-Terrestrial Physics, 123: 39-47. - Plauborg, F., 2002. Simple model for 10 cm soil temperature in different soils with short grass. European Journal of Agronomy, 17: 173-179. - Sabziparvar, A. A., Zare Abyaneh, H. and Bayat Varkeshi, M., 2010. A model comparison between predicted soil temperatures using ANFIS model and regression methods in three different climates. Journal of Water and Soil (Agricultural Sciences and Technology), 24 (2): 274-285. - Schaetzl, R. J. and Tomczak, D. M., 2001. Winter time temperature in the fine-textured soils of the Saginaw valley, Michigan. Great Lakes Geography, 8: 87-98. - Schap, M. G., Leij, F. J. and Van Genuchten, M. T., 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Journal of Soil Science Society of America, 62: 847-855. - Tabari, H., Hosseinzadeh Talaee, P. and Willems, P., 2015. Short-term forecasting of soil temperature using artificial neural network. Journal of Meteorological Applications, 22: 576-585. - Tabari, H., Sabzi parvar, A. A. and Ahmadi, M., 2011. Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorology and Atmospheric Physics, 110: 135-142. - Todhunter, P. E., 2001. A hydro- climatological analysis of the Red River of the North snowmelt fiood catastrophone of 1997. American Water Resources, 37: 1263-1278. - Wu, W., Tang, X. P, Guo, N. J., Yang, C., Liu, H. B. and Shang, Y. F., 2013. Spatio-temporal modeling of monthly soil temperature using artificial neural networks. Theoretical and Applied Climatology, 113: 481-494. - Yadav, D., Naresh, R. and Sharma, V., 2011. Stream flow forecasting using Levenberg-Marquardt algorithm approach. International Journal of Water Resources and Environmental Engineering, 3 (1): 30-40. - Yasin, Y., Ahmad, F., Ghaffari Moghaddam, M. and Khajeh, M., 2014. Application of a hybrid artificial neural network–genetic algorithm approach to optimize the lead ions removal from aqueous solutions using intercalated tartrate-Mg–Al layered double hydroxides. Environmental Nanotechnology, Monitoring and Management, 1(2): 2-7. - Zheng, D. Raymond Hunt J. E. and Running, S. W., 1993. A daily soil temperature model based on air temperature and precipitation for continental applications. Journal of Climate Research, 2: 183-191. - Zuzel, J. R. and Pikul, J. L., 1987. Infitltration into a seasonally frozen agricultural soil. Journal of Soil Water Conservation, 42: 447-450. | ||
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