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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 | ||
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