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پیش‌بینی تبخیر-تعرق روزانه برنج در مقیاس مزرعه با استفاده از رویکرد یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 12، اسفند 1401، صفحه 2793-2807 اصل مقاله (1.68 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.350978.669391 | ||
| نویسندگان | ||
| هما نوغان کار1؛ محمود رائینی1؛ محمد علی غلامی سفیدکوهی* 1؛ مجید مبینی2 | ||
| 1گروه مهندسی آب، دانشکده مهندسی زراعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 2دانشکده مهندسی برق، موسسه آموزش عالی صنعتی مازندران، بابل، ایران | ||
| چکیده | ||
| پیش‌بینی کوتاه‌مدت تبخیر-تعرق روزانه گیاه در کشاورزی دقیق و مدیریت آبیاری اهمیت فراوانی دارد. در این مقاله، روشی برای پیش‌بینی کوتاه مدت نقشه‌های تبخیر-تعرق روزانه گیاه برنج با استفاده از تصاویر ماهواره‌ای و الگوریتم‌های یادگیری ماشین ارائه شده است. پس از تلفیق باندهای تصاویر لندست 8 و مودیس با استفاده از روش STARFM، تصاویر تبخیر-تعرق روزانه به کمک الگوریتم METRIC تولید و برای پیش‌بینی نقشه‌های تبخیر-تعرق روزهای بعدی به عنوان ورودی به ماشین بردار ارتباط (RVM) و حافظه کوتاه-مدت طولانی (LSTM) اعمال شدند. دو سناریو برای پیش‌بینی در نظر گرفته شد. در سناریوی اول، با استفاده از یک تصویر و یک گام زمانی شش روزه، تصویر شش روز بعد پیش‌بینی شد. در سناریوی دوم، پیش‌بینی برای روزهای متوالی تا شش روز انجام شد. ضریب همبستگی بین مقادیر پیش‌بینی شده توسط RVM و مقادیر واقعی برای سناریوی اول و دوم به ترتیب 89/0 و 84/0 بدست آمد که نشان دهنده دقت قابل قبول این دو سناریو در پیش‌بینی تبخیر-تعرق است. در سناریوی نخست، مقادیر R2 برای دو روش RVM و LSTM به ترتیب برابر با 8/0 و 59/0 بدست آمد که نشان می‌دهد RVM در مقایسه با LSTM از دقت بیشتری برای پیش‌بینی تبخیر-تعرق برخوردار است. مقدار RMSE برای RVM در سناریوی اول و دوم به ترتیب برابر با 56/0 و 82/0 و مقدار MAE نیز به ترتیب برابر با 43/0 و 66/0 بدست آمد که نشان از خطای کمتر ناشی از پیکره‌بندی انجام شده در سناریوی اول می‌باشد. | ||
| کلیدواژه‌ها | ||
| الگوریتم METRIC؛ تلفیق تصاویر ماهواره‌ای؛ ماشین بردار ارتباط؛ LSTM | ||
| عنوان مقاله [English] | ||
| Prediction of daily evapotranspiration images of rice using machine learning | ||
| نویسندگان [English] | ||
| Homa Noghankar1؛ Mahmoud Raeini1؛ Mohammad Ali Gholami Sefidkouhi1؛ Majid mobini2 | ||
| 1Department of water engineering, faculty of agricultural engineering, Sari agricultural sciences and natural resources university, sari, Iran | ||
| 2Department of Electrical and Computer Engineering, Mazandaran Institute of Technology, Babol, Iran | ||
| چکیده [English] | ||
| Short-term prediction of daily plant evapotranspiration (ET) is of great importance in precision agriculture and irrigation management. In this paper, a method for short-term prediction of daily ET maps of rice is presented using satellite images and machine learning algorithms. After merging the bands of Landsat-8 and MODIS images using the STARFM method, daily ET images were produced using the METRIC algorithm and used to predict the ET maps of the following days as input to the relation vector machine (RVM) and long short-term memory (LSTM). Two scenarios were considered for prediction. In the first scenario, model is trained using image of nth day of the growth period as input, and the n+6th day's image as target. Using this configuration, the model can predict ET images at a six-day timestep. In the second scenario, the forecast was made for consecutive days up to six days. The correlation coefficient between the values obtained by RVM and the values calculated by METRIC for the first and second scenario were 0.89 and 0.84, respectively, which indicates the acceptable accuracy of these two scenarios in predicting ET. In the first scenario, R2 values for RVM and LSTM methods were 0.8 and 0.59, respectively, which shows that RVM is more accurate for evapotranspiration prediction compared to LSTM. The values of RMSE for RVM in the first and second scenarios were 0.56 and 0.82, respectively, and the values of MAE were 0.43 and 0.66, respectively, which indicates a lower error in the configuration of the first scenario. | ||
| کلیدواژه‌ها [English] | ||
| LSTM METRIC algorithm, Relevance Vector Machine, Satellite image fusion | ||
| مراجع | ||
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