Developing a Radial Basis Function Neural Networks to Predict the Working Days for Tillage Operation in Crop Production
محورهای موضوعی : Farm Management
ارمغان کوثری مقدم
1
(گروه مهندسی بیوسیستم، دانشگاه تبریز، تبریز، ایران)
عباس روحانی
2
(گروه مهندسی مکانیک بیوسیستم، دانشگاه فردوسی مشهد، مشهد، ایران)
Lobat Kosari-Moghaddam
3
(گروه علوم و مهندسی آب، دانشگاه فردوسی مشهد، مشهد، ایران)
مهدی اسماعیل پور تروجنی
4
(گروه مهندسی مکانیک بیوسیستم، دانشگاه فردوسی مشهد، مشهد، ایران)
کلید واژه: Artificial Neural Network, multiple linear regression, probability of working days, redial basis function,
چکیده مقاله :
The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily basis. The PWD was considered to be the output of the developed models. Performance criteria were RMSE, MAPE, and R2. Results showed that the R2-valuewas 0.78 and 0.99 for MLR and RBF models, respectively. Both models had acceptable performance, but the RBF model was more accurate than the MLR model. The RMSE and MAPE values for the RBF model were lower than those for the MLR model. Thus, the RBF model was selected as the suitable model for predicting PWD. Moreover, the results of these models were compared to the prior soil moisture model. It was indicated that the results of the studied models had a good agreement with the results of the soil moisture model. However, the RBF model had the highest R2 (99%). In conclusion, the developed RBF model could be used to predict the probability of working days in terms of agricultural management policies.
هدف از این مطالعه تعیین احتمال روزهای کاری (PWD) برای عملیات خاک‌ورزی با استفاده از داده‌های هواشناسی و به کمک روش مدلسازی رگرسیونی خطی چندگانه (MLR) و شبکه عصبی مصنوعی توابع پایه‌ای شعاعی (RBF) بوده است. در هر دو مدل، هفت متغیر شامل دمای متوسط، بیشینه و کمینه، رطوبت نسبی، بارندگی، سرعت باد و تبخیر و تعرق بر پایه روزانه به عنوان پارامترهای ورودی در نظر گرفته شدند. احتمال روزهای کاری نیز به عنوان خروجی مدل‌های ایجاد شده، در نظر گرفته شد. معیارهای عملکردی شامل مجذور مربعات خطا (RMSE)، میانگین درصد خطای مطلق (MAPE) و ضریب تبیین (R2) بودند. نتایج نشان داد که مقادیر R2 برای مدل‌های MLR و RBF به ترتیب برابر 78/0 و 99/0 بوده است. هر دو مدل عملکرد قابل قبولی داشتند؛ اما مدل RBF عملکرد دقیق‌تری نسبت به مدل MLR داشت. مقادیر RMSE و MAPE نیز برای مدل RBF کمتر از مدل MLR بدست آمد. بنابراین مدل RBF به عنوان بهترین مدل برای پیش‌بینی احتمال روزهای کاری انتخاب شد. علاوه بر این، نتایج این مدل‌ها با یک مدل رطوبت خاک که پیش‌تر ارائه شده بود، مقایسه شد. نشان داده شد که نتایج مدل‌های مورد مطالعه با نتایج مدل رطوبت خاک سازگاری خوبی داشته است، اگرچه که مدل RBF بالاترین ضریب تبیین را داشت (R2=99%). در نهایت می‌توان بیان نمود که مدل RBF ایجاد شده می‌تواند برای پیش‌بینی احتمال روزهای کاری در راستای سیاست‌گذاری‌های مدیریتی در بخش کشاورزی مورد استفاده قرار گیرد.
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