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بوربور, هانیه, عبداللهی پور, محمد, عبدالهی, حجت, مشعل, محمود. (1402). برآورد عملکرد جو براساس داده‌های سنجش از دور و الگوریتم‌های یادگیری ماشین XGBoost و SVM. , 13(4), 1119-1137. doi: 10.22059/jwim.2023.360327.1083
هانیه بوربور; محمد عبداللهی پور; حجت عبدالهی; محمود مشعل. "برآورد عملکرد جو براساس داده‌های سنجش از دور و الگوریتم‌های یادگیری ماشین XGBoost و SVM". , 13, 4, 1402, 1119-1137. doi: 10.22059/jwim.2023.360327.1083
بوربور, هانیه, عبداللهی پور, محمد, عبدالهی, حجت, مشعل, محمود. (1402). 'برآورد عملکرد جو براساس داده‌های سنجش از دور و الگوریتم‌های یادگیری ماشین XGBoost و SVM', , 13(4), pp. 1119-1137. doi: 10.22059/jwim.2023.360327.1083
بوربور, هانیه, عبداللهی پور, محمد, عبدالهی, حجت, مشعل, محمود. برآورد عملکرد جو براساس داده‌های سنجش از دور و الگوریتم‌های یادگیری ماشین XGBoost و SVM. , 1402; 13(4): 1119-1137. doi: 10.22059/jwim.2023.360327.1083

برآورد عملکرد جو براساس داده‌های سنجش از دور و الگوریتم‌های یادگیری ماشین XGBoost و SVM

مدیریت آب و آبیاری
دوره 13، شماره 4، دی 1402، صفحه 1119-1137    اصل مقاله (832.33 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/jwim.2023.360327.1083
نویسندگان
هانیه بوربور1؛ محمد عبداللهی پور* 1؛ حجت عبدالهی2؛ محمود مشعل1
1گروه مهندسی آب، دانشکده فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، تهران، ایران.
2گروه مهندسی کامپیوتر و الکترونیک، دانشگاه دنور، دنور، کلرادو، آمریکا.
چکیده
در سال‌های اخیر، بهره‌گیری از الگوریتم‌های یادگیری ماشین شیوه‌ای امیدوارکننده در بهبود پیش‌بینی‌‌های عملکرد محصولات زراعی بوده است. این پژوهش با هدف ارزیابی برآورد عملکرد محصول جو آبی و دیم و نیز عملکرد کل جو تولیدی در مراکز استان‌های کشور با استفاده از داده‌های سنجش از دور و روش‌های یادگیری ماشین شامل  XGBoostو SVM انجام شد. نتایج نشان داد که با استفاده از داده‌های اقلیمی، شاخص‌های خشکسالی و شاخص‌های گیاهی سنجش از دوری و الگوریتم XGBoost و SVM می‌توان به‌طور قابل‌قبولی برآورد عملکرد محصول جو را در مناطق مختلف کشور با اقلیم‌های متفاوت انجام داد. میزان خطای RMSE برای هر دو مدل، در حد قابل‌قبول بین 41/0 تا 77/0 تن در هکتار قرار داشت. با توجه به مقادیر ضریب تعیین R2  که برای الگوریتم‌های XGBoost و SVR در مدل‌سازی عملکرد کشت دیم به‌ترتیب برابر 2/0 و 22/0، در عملکرد آبی برابر 52/0 و 55/0 و برای حالت ترکیبی جو آبی و دیم برابر 66/0 و 65/0 به‌دست آمده است، می‌توان گفت که نتایج برای هر دو الگوریتم در برآورد محصول دیم نامناسب‌تر از برآورد جو آبی و نیز ترکیب جو آبی و دیم بوده است. کرنل RBF به‌عنوان مناسب‌ترین کرنل برای استفاده در الگوریتم SVM انتخاب شد. هم‌چنین در این پژوهش ضمن بررسی اثرات تغییر نسبت تقسیم داده‌های مراحل آموزش و آزمون، پارامترهای بارش، دما و تبخیر و تعرق به‌عنوان مهم‌ترین پارامترهای مؤثر بر عملکرد محصول جو برای هر دو الگوریتم در حالت‌های مختلف موردبررسی تعیین شد.
کلیدواژه‌ها
الگوریتم‌‌های یادگیری ماشین؛ پیش‌بینی عملکرد؛ سنجش از دور؛ محصول جو
عنوان مقاله [English]
Barley Yield Forecasting Based on Remote Sensing Data and Xgboost and SVM Machine Learning Algorithms
نویسندگان [English]
Hanie Bourbour1؛ Mohammad Abdolahipour1؛ Hojjat Abdollahi2؛ Mahmoud Mashal1
1Department of Water Engineering, Faculty of Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.
2Department of Electrical and Computer Engineering, University of Denver, Denver, Colorado, US.
چکیده [English]
In recent years, the use of machine learning algorithms has been a promising way to improve crop yield predictions, especially when using non-linear relationships. This research was conducted with the aim of evaluating the yield estimation of irrigated and rainfed barley as well as the total yield of barley produced in the provincial centers of Iran using remote sensing data and machine learning methods including XGBoost and SVM. The results showed that by using climatic data, drought indices and plant indices of remote sensing and also XGBoost and SVM algorithms, it is possible to reliably estimate barley yield in different regions of the country with different climates. In general, the RMSE error obtained for both models was acceptable (0.41 and 0.77 t/ha). The R2 determination coefficient values for XGBoost and SVR algorithms in modeling rainfed barley cultivation performance were equal to 0.2 and 0.22 respectively, in irrigated barley performance were equal to 0.52 and 0.55 and for total barley were equal to 0.66 and 0.65, indicating that the rainfed yield modeling were not as suitable as irrigated and total barely yield modeling. The RBF kernel was chosen as the best kernel to use for the SVM algorithm. Also, in this research, while examining the effects of change in train and test data dividing, the parameters of precipitation, temperature, and evapotranspiration were determined as the most important parameters affecting the performance of the barley yield for both algorithms in different evaluated conditions.
کلیدواژه‌ها [English]
Barely production, Machine learning algorithms, Remote sensing, Yield prediction
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آمار
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